------------------------------------------------------------------------------------------------------------------------------------
      name:  plog_33
       log:  /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/code/do/10_analysis_individual.log
  log type:  text
 opened on:  19 Sep 2022, 11:40:25

. *************************************************
. ********INDIVIDUAL Level Analysis****************
. *************************************************
. 
. * directory definitions 
. 
.         project, doinfo
project PlaceBased_analysis > Project Name: PlaceBased_analysis
project PlaceBased_analysis > Project Dir.: /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication
project PlaceBased_analysis > Build start : 19sep2022, 11:38:37
project PlaceBased_analysis > Do-file Name: 10_analysis_individual.do
project PlaceBased_analysis > Enclosing do-files:
project PlaceBased_analysis >     PlaceBased_analysis.do

.         global pdir "`r(pdir)'"                                                 // the project's main dir.

.         global dofile "`r(dofile)'"                                             // do-file's stub name

.         global data_original = "$pdir/data_original"  //data directory for coded data 

.         global data_coded = "$pdir/data_coded"  //data directory for coded data 

.         global figures = "$pdir/results/figures"  //data directory for figures

.         global tables = "$pdir/results/tables"  //data directory for tables

. 
. * 
.         // report any data we create previously and we do need here: 
.         project, uses("$data_coded/placebased_individual.dta")
project PlaceBased_analysis > do-file uses: "data_coded/placebased_individual.dta" filesig(836765086:58113503)

. 
. * open individual level panel data:
.         
.         use "$data_coded/placebased_individual.dta", clear 

.         xtset 

Panel variable: id (strongly balanced)
 Time variable: wave, 1 to 4, but with gaps
         Delta: 1 unit

. 
. 
. 
. 
. 
. ***MAINBODY***
. 
. * estimation: 
. 
.         // define locals: 
.         local control_soc eco_general unemployed_3 female_3 age_3 edu_3 religious_3 

.         local control_pol m5s_1 pd_1 eco_general_1 lr_no_1 polinterest_1 talk_politics_1 pol_complex_1 internal_efficacy_1

.         // OLS estimates (tab 2): 
.         eststo clear

.         eststo: reghdfe referendum_no_wunsure m5s_wn_wave_157, absorb(comune_id post) cluster(id comune_id)
(dropped 28 singleton observations)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      5,254
Absorbing 2 HDFE groups                           F(   1,   1015) =       9.78
Statistics robust to heteroskedasticity           Prob > F        =     0.0018
                                                  R-squared       =     0.3322
                                                  Adj R-squared   =     0.1717
Number of clusters (id)      =      2,671         Within R-sq.    =     0.0015
Number of clusters (comune_id) =      1,016       Root MSE        =     0.4533

                          (Std. err. adjusted for 1,016 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_no~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0734752   .0234995     3.13   0.002      .027362    .1195885
          _cons |   .3925365   .0202473    19.39   0.000     .3528051    .4322679
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |      1016        1016           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

.         eststo: reghdfe referendum_no_wunsure m5s_wn_wave_157 `control_soc', absorb(comune_id post) cluster(id comune_id)
(dropped 36 singleton observations)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      5,206
Absorbing 2 HDFE groups                           F(   7,   1005) =      54.69
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3924
                                                  Adj R-squared   =     0.2454
Number of clusters (id)      =      2,656         Within R-sq.    =     0.0901
Number of clusters (comune_id) =      1,006       Root MSE        =     0.4329

                          (Std. err. adjusted for 1,006 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_no~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0804586   .0248947     3.23   0.001     .0316071    .1293101
    eco_general |  -.1614127   .0096877   -16.66   0.000    -.1804232   -.1424023
   unemployed_3 |  -.0463387   .0200586    -2.31   0.021    -.0857002   -.0069772
       female_3 |  -.0612806   .0201576    -3.04   0.002    -.1008364   -.0217249
          age_3 |  -.0003167   .0006573    -0.48   0.630    -.0016065    .0009731
          edu_3 |  -.0017277   .0090798    -0.19   0.849    -.0195452    .0160898
    religious_3 |  -.0703826   .0220881    -3.19   0.001    -.1137267   -.0270385
          _cons |   .9430899   .0651826    14.47   0.000     .8151804    1.070999
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |      1006        1006           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

.         eststo: reghdfe referendum_no_wunsure m5s_wn_wave_157 `control_soc' `control_pol', absorb(comune_id post) cluster(id comun
> e_id)
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(  15,    855) =      43.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4535
                                                  Adj R-squared   =     0.3045
Number of clusters (id)      =      2,064         Within R-sq.    =     0.1448
Number of clusters (comune_id) =        856       Root MSE        =     0.4165

                                (Std. err. adjusted for 856 clusters in id comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
referendum_no_wun~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    m5s_wn_wave_157 |   .0740575   .0278654     2.66   0.008      .019365      .12875
        eco_general |  -.1658623   .0131371   -12.63   0.000     -.191647   -.1400776
       unemployed_3 |  -.0272567   .0240539    -1.13   0.257    -.0744683    .0199549
           female_3 |   -.017702   .0216696    -0.82   0.414     -.060234    .0248299
              age_3 |  -.0011894   .0007806    -1.52   0.128    -.0027215    .0003426
              edu_3 |  -.0133796    .009985    -1.34   0.181    -.0329776    .0062184
        religious_3 |  -.0404475   .0269409    -1.50   0.134    -.0933255    .0124305
              m5s_1 |   .0727209   .0327644     2.22   0.027     .0084128    .1370291
               pd_1 |  -.1257669   .0273588    -4.60   0.000    -.1794652   -.0720685
      eco_general_1 |  -.0138232    .013586    -1.02   0.309     -.040489    .0128427
            lr_no_1 |   .0071262   .0364054     0.20   0.845    -.0643282    .0785807
      polinterest_1 |   .0428628   .0204003     2.10   0.036     .0028222    .0829034
    talk_politics_1 |   .0237551   .0092145     2.58   0.010     .0056694    .0418407
      pol_complex_1 |   .0031949   .0119043     0.27   0.788    -.0201703      .02656
internal_efficacy_1 |   .0134409   .0111353     1.21   0.228    -.0084148    .0352965
              _cons |   .7918635   .1043311     7.59   0.000     .5870883    .9966386
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

.         eststo: reghdfe referendum_no_wunsure m5s_wn_wave_157, absorb(id post) cluster(id comune_id)
(dropped 116 singleton observations)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      5,166
Absorbing 2 HDFE groups                           F(   1,   1011) =       8.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0028
                                                  R-squared       =     0.7551
                                                  Adj R-squared   =     0.5097
Number of clusters (id)      =      2,583         Within R-sq.    =     0.0036
Number of clusters (comune_id) =      1,012       Root MSE        =     0.3487

                          (Std. err. adjusted for 1,012 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_no~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0683989   .0228646     2.99   0.003     .0235314    .1132664
          _cons |   .3966385   .0196044    20.23   0.000     .3581686    .4351084
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          id |      2583        2583           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)

.         ebalance m5s_ever `control_soc' `control_pol'


Data Setup
Treatment variable:   m5s_ever
Covariate adjustment: eco_general unemployed_3 female_3 age_3 edu_3 religious_3 m5s_1 pd_1 eco_general_1 lr_no_1 polinterest_1 talk_
> politics_1 pol_complex_1 internal_efficacy_1 

Optimizing...
Iteration 1: Max Difference = 11441.4483
Iteration 2: Max Difference = 4207.68638
Iteration 3: Max Difference = 1546.53494
Iteration 4: Max Difference = 567.554361
Iteration 5: Max Difference = 207.41382
Iteration 6: Max Difference = 74.9425038
Iteration 7: Max Difference = 26.2543719
Iteration 8: Max Difference = 8.46000096
Iteration 9: Max Difference = 2.185187
Iteration 10: Max Difference = .332235182
Iteration 11: Max Difference = .01710886
Iteration 12: Max Difference = .000080297
maximum difference smaller than the tolerance level; convergence achieved


Treated units: 4018    total of weights: 4018
Control units: 2233    total of weights: 4018


Before: without weighting

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |     2.351      .8945      .1253 
unemployed_3 |     .4567      .2482      .1739 |     .4971      .2501     .01164 
    female_3 |     .4216      .2439      .3175 |     .4339      .2457      .2666 
       age_3 |     52.52      234.2    -.03322 |     51.26      231.4     .05314 
       edu_3 |     5.183      1.754     -1.227 |     4.815      2.236     -.9771 
 religious_3 |     .7857      .1684     -1.393 |     .8186      .1485     -1.654 
       m5s_1 |     .2165      .1697      1.377 |     .2262      .1751      1.309 
        pd_1 |     .2805      .2019      .9773 |     .2642      .1945       1.07 
eco_genera~1 |     1.895      .7781      .8958 |      1.83      .6965      .9648 
     lr_no_1 |     .1147      .1016      2.418 |     .1303      .1134      2.196 
polinteres~1 |     3.179      .4515     -.3309 |     3.109      .4841     -.4292 
talk_polit~1 |     4.716      2.031      -.768 |     4.679      2.099      -.703 
pol_comple~1 |     2.434      .9562       .118 |     2.338      .9988      .2854 
internal_e~1 |     2.153       .988      .4234 |      2.18      .9894      .3298 


After:  _webal as the weighting variable

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |      2.41      .8877      .0831 
unemployed_3 |     .4567      .2482      .1739 |     .4567      .2482      .1739 
    female_3 |     .4216      .2439      .3175 |     .4216       .244      .3175 
       age_3 |     52.52      234.2    -.03322 |     52.52      228.6     -.0179 
       edu_3 |     5.183      1.754     -1.227 |     5.183      1.652     -1.291 
 religious_3 |     .7857      .1684     -1.393 |     .7857      .1684     -1.393 
       m5s_1 |     .2165      .1697      1.377 |     .2165      .1697      1.377 
        pd_1 |     .2805      .2019      .9773 |     .2805      .2019      .9773 
eco_genera~1 |     1.895      .7781      .8958 |     1.895      .7354      .9299 
     lr_no_1 |     .1147      .1016      2.418 |     .1147      .1016      2.418 
polinteres~1 |     3.179      .4515     -.3309 |     3.179      .4567     -.4357 
talk_polit~1 |     4.716      2.031      -.768 |     4.716      2.013      -.709 
pol_comple~1 |     2.434      .9562       .118 |     2.434       1.02      .1746 
internal_e~1 |     2.153       .988      .4234 |     2.153      .9738      .3348 

.         eststo: reghdfe referendum_no_wunsure m5s_wn_wave_157 [aweight=_webal], absorb(comune_id post) cluster(id comune_id)
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(   1,    855) =       7.12
Statistics robust to heteroskedasticity           Prob > F        =     0.0077
                                                  R-squared       =     0.4178
                                                  Adj R-squared   =     0.2622
Number of clusters (id)      =      2,064         Within R-sq.    =     0.0013
Number of clusters (comune_id) =        856       Root MSE        =     0.4288

                            (Std. err. adjusted for 856 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_no~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0736405   .0275901     2.67   0.008     .0194883    .1277927
          _cons |   .4196586    .018875    22.23   0.000     .3826119    .4567054
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)

.         eststo: reghdfe referendum_no_wunsure m5s_wn_wave_157 [aweight=_webal], absorb(id post) cluster(id comune_id)
(dropped 81 singleton observations)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      4,012
Absorbing 2 HDFE groups                           F(   1,    854) =       6.47
Statistics robust to heteroskedasticity           Prob > F        =     0.0112
                                                  R-squared       =     0.7854
                                                  Adj R-squared   =     0.5703
Number of clusters (id)      =      2,006         Within R-sq.    =     0.0032
Number of clusters (comune_id) =        855       Root MSE        =     0.3271

                            (Std. err. adjusted for 855 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_no~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0692544   .0272284     2.54   0.011      .015812    .1226968
          _cons |   .4207595   .0184929    22.75   0.000     .3844626    .4570563
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          id |      2006        2006           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est6 stored)

.         ** create a LaTeX table:
.         estfe est*, labels(id "Individual FE" post "Wave FE" comune_id "Municipality FE")

.         ** save the table:
.         esttab est* using "$tables/tab2_ols_individual.tex", replace  ///
>         indicate( `r(indicate_fe)' "Socio-economic controls=unemployed_3" "Political controls=pd_1", labels(\checkmark)) ///
>         se nobaselevels nostar label se(2) b(2) ///
>         drop(eco_general female_3 age_3 edu_3 religious_3 polinterest_1 talk_politics_1 eco_general_1 lr_no_1 pol_complex_1 intern
> al_efficacy_1) ///
>         stats(N N_clust2 r2_a r2_a_within rmse, fmt(%9.0f %9.0f %9.2f %9.2f %9.2f)  labels("Obs" "Municipalities" "adj.R\$^2$" "ad
> j.R\$^2$ (within)" "RMSE")) ///
>         note("\emph{Note:} Clustered standard errors by individual$\times \$ municipality in parentheses. Controls omitted from ta
> ble: economy retrospective (1-5), unemployed (0,1), female (0,1), age (18-88), education (1-7), religiosity (0,1), PD voter in 201
> 3 (0,1), political interest (1-4), talk politics (1-6), explicitly no left-right self-placement (0,1), politics too complex (1-4),
>  internal efficacy (1-4). For entropy balancing we use only variables asked in the 2013 post election study as outlined in Figure 
> \ref{fig:iv_balance}.") ///
>         substitute(_ _) 
(output written to /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/results/tables/tab2_ols_individual.tex)

. 
.         // one town simulation: 100 inhabitants, 0 vs 1 event, 10% rsvp
.         reghdfe referendum_no_wunsure m5s_wn_wave_157, absorb(id post) cluster(id comune_id)
(dropped 116 singleton observations)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      5,166
Absorbing 2 HDFE groups                           F(   1,   1011) =       8.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0028
                                                  R-squared       =     0.7551
                                                  Adj R-squared   =     0.5097
Number of clusters (id)      =      2,583         Within R-sq.    =     0.0036
Number of clusters (comune_id) =      1,012       Root MSE        =     0.3487

                          (Std. err. adjusted for 1,012 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_no~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0683989   .0228646     2.99   0.003     .0235314    .1132664
          _cons |   .3966385   .0196044    20.23   0.000     .3581686    .4351084
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          id |      2583        2583           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         margins, at(m5s_wn_wave_157=(0 0.40546511))

Adjusted predictions                                     Number of obs = 5,166
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: m5s_wn_wave_157 =        0
2._at: m5s_wn_wave_157 = .4054651

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3966385   .0196044    20.23   0.000     .3582146    .4350624
          2  |   .4243719   .0103336    41.07   0.000     .4041184    .4446253
------------------------------------------------------------------------------

.         dis .4233418 -  .3943116 
.0290302

.         ** .0290302
. 
.         // "manipulation" test (fig 6b)
.         ** estimate models for M5S:
.         cls

.         eststo clear

.         foreach var of varlist web_politican_4-web_event_4 {
  2.         gen `var'_web=m5s_ref_yn 
  3.         eststo: reghdfe `var' `var'_web `control_soc' `control_pol' [aweight=_webal], absorb(province_1) cluster(comune_id)
  4.         drop `var'_web
  5.         }
(3,752 missing values generated)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,046
Absorbing 1 HDFE group                            F(  15,    866) =       7.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1308
                                                  Adj R-squared   =     0.0756
                                                  Within R-sq.    =     0.0536
Number of clusters (comune_id) =        867       Root MSE        =     0.8183

                                   (Std. err. adjusted for 867 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
    web_politican_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
web_politican_4_web |   .0293063    .043304     0.68   0.499    -.0556869    .1142994
        eco_general |   .0012564   .0281621     0.04   0.964    -.0540175    .0565303
       unemployed_3 |    .034304   .0443149     0.77   0.439    -.0526731     .121281
           female_3 |  -.0283043   .0381214    -0.74   0.458    -.1031254    .0465168
              age_3 |  -.0031585   .0016108    -1.96   0.050    -.0063202    3.07e-06
              edu_3 |  -.0168656   .0177162    -0.95   0.341    -.0516372    .0179061
        religious_3 |   .1814085   .0444142     4.08   0.000     .0942364    .2685806
              m5s_1 |  -.0922511   .0511238    -1.80   0.072    -.1925921    .0080899
               pd_1 |  -.0899911   .0453381    -1.98   0.047    -.1789765   -.0010057
      eco_general_1 |   .0921675   .0273882     3.37   0.001     .0384126    .1459224
            lr_no_1 |  -.0715481   .0706697    -1.01   0.312    -.2102521    .0671559
      polinterest_1 |   .1696229   .0366901     4.62   0.000      .097611    .2416347
    talk_politics_1 |   .0311689   .0150552     2.07   0.039     .0016201    .0607178
      pol_complex_1 |  -.0185175   .0217392    -0.85   0.395    -.0611851    .0241502
internal_efficacy_1 |   .0485473   .0223322     2.17   0.030     .0047158    .0923788
              _cons |   .7820795   .1974711     3.96   0.000     .3945016    1.169658
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |       108           0         108     |
-----------------------------------------------------+
(est1 stored)
(3,752 missing values generated)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,044
Absorbing 1 HDFE group                            F(  15,    863) =       7.24
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1332
                                                  Adj R-squared   =     0.0782
                                                  Within R-sq.    =     0.0574
Number of clusters (comune_id) =        864       Root MSE        =     0.9669

                                   (Std. err. adjusted for 864 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
     web_socmedia_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
 web_socmedia_4_web |   .1109553   .0536802     2.07   0.039     .0055963    .2163143
        eco_general |   .0249299   .0316807     0.79   0.432    -.0372502    .0871101
       unemployed_3 |   .0191538   .0514818     0.37   0.710    -.0818905    .1201981
           female_3 |  -.0098756    .048701    -0.20   0.839    -.1054619    .0857106
              age_3 |  -.0083413   .0018906    -4.41   0.000    -.0120521   -.0046306
              edu_3 |  -.0137794   .0220761    -0.62   0.533    -.0571085    .0295498
        religious_3 |   .2267609   .0534133     4.25   0.000     .1219256    .3315961
              m5s_1 |   -.036291   .0657275    -0.55   0.581    -.1652954    .0927135
               pd_1 |  -.1784955   .0565227    -3.16   0.002    -.2894335   -.0675575
      eco_general_1 |   .0406505    .030532     1.33   0.183    -.0192752    .1005761
            lr_no_1 |  -.0613712   .0818287    -0.75   0.453    -.2219777    .0992352
      polinterest_1 |   .1739606   .0424597     4.10   0.000     .0906243    .2572969
    talk_politics_1 |    .057436   .0208263     2.76   0.006     .0165598    .0983122
      pol_complex_1 |  -.0322261   .0257873    -1.25   0.212    -.0828392     .018387
internal_efficacy_1 |   .0504927   .0248005     2.04   0.042     .0018163     .099169
              _cons |   1.136306    .220948     5.14   0.000     .7026475    1.569964
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |       108           0         108     |
-----------------------------------------------------+
(est2 stored)
(3,752 missing values generated)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,043
Absorbing 1 HDFE group                            F(  15,    863) =       5.80
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1207
                                                  Adj R-squared   =     0.0648
                                                  Within R-sq.    =     0.0515
Number of clusters (comune_id) =        864       Root MSE        =     0.9275

                                   (Std. err. adjusted for 864 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
        web_video_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    web_video_4_web |   .1353717   .0549752     2.46   0.014     .0274709    .2432724
        eco_general |  -.0063512   .0292663    -0.22   0.828    -.0637928    .0510903
       unemployed_3 |  -.0551133   .0493124    -1.12   0.264    -.1518996     .041673
           female_3 |   .0382261   .0455252     0.84   0.401    -.0511269    .1275791
              age_3 |  -.0015012   .0018525    -0.81   0.418    -.0051371    .0021347
              edu_3 |   .0335344    .020802     1.61   0.107     -.007294    .0743628
        religious_3 |   .1434002   .0550444     2.61   0.009     .0353638    .2514367
              m5s_1 |   .0647239   .0604792     1.07   0.285    -.0539795    .1834274
               pd_1 |   .0033959   .0558738     0.06   0.952    -.1062686    .1130604
      eco_general_1 |   .0935487   .0317688     2.94   0.003     .0311955    .1559019
            lr_no_1 |  -.0474751   .0741648    -0.64   0.522    -.1930397    .0980894
      polinterest_1 |   .1309799   .0414893     3.16   0.002     .0495481    .2124117
    talk_politics_1 |   .0769614   .0218838     3.52   0.000     .0340098    .1199131
      pol_complex_1 |  -.0048002   .0248436    -0.19   0.847    -.0535611    .0439608
internal_efficacy_1 |   .0266991    .024729     1.08   0.281    -.0218369    .0752351
              _cons |   .6557026   .2155399     3.04   0.002     .2326589    1.078746
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |       108           0         108     |
-----------------------------------------------------+
(est3 stored)
(3,752 missing values generated)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,048
Absorbing 1 HDFE group                            F(  15,    863) =       8.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1409
                                                  Adj R-squared   =     0.0864
                                                  Within R-sq.    =     0.0687
Number of clusters (comune_id) =        864       Root MSE        =     0.9710

                                   (Std. err. adjusted for 864 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
        web_share_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    web_share_4_web |   .0889887   .0528078     1.69   0.092     -.014658    .1926354
        eco_general |  -.0770494   .0327406    -2.35   0.019    -.1413099   -.0127889
       unemployed_3 |  -.0168641   .0530278    -0.32   0.751    -.1209426    .0872145
           female_3 |  -.0583812      .0515    -1.13   0.257     -.159461    .0426986
              age_3 |   .0026049   .0017788     1.46   0.143    -.0008863    .0060962
              edu_3 |    .030425   .0204561     1.49   0.137    -.0097245    .0705745
        religious_3 |   .1252882   .0605534     2.07   0.039     .0064391    .2441373
              m5s_1 |   .1020777   .0658274     1.55   0.121    -.0271229    .2312782
               pd_1 |  -.0219254   .0593715    -0.37   0.712    -.1384548    .0946041
      eco_general_1 |   .0911671   .0308797     2.95   0.003     .0305589    .1517753
            lr_no_1 |     .01412   .0824639     0.17   0.864    -.1477333    .1759732
      polinterest_1 |   .2282467   .0442474     5.16   0.000     .1414016    .3150917
    talk_politics_1 |   .0782964   .0203867     3.84   0.000     .0382831    .1183098
      pol_complex_1 |  -.0103668   .0243137    -0.43   0.670    -.0580877     .037354
internal_efficacy_1 |    .026528   .0287161     0.92   0.356    -.0298336    .0828896
              _cons |   .3738934   .2227267     1.68   0.094     -.063256    .8110427
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |       108           0         108     |
-----------------------------------------------------+
(est4 stored)
(3,752 missing values generated)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 1 HDFE group                            F(  15,    859) =       7.10
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1592
                                                  Adj R-squared   =     0.1057
                                                  Within R-sq.    =     0.0674
Number of clusters (comune_id) =        860       Root MSE        =     0.9324

                                   (Std. err. adjusted for 860 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
      web_discuss_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
  web_discuss_4_web |   .1513557   .0528157     2.87   0.004     .0476928    .2550185
        eco_general |  -.0097018   .0296233    -0.33   0.743    -.0678443    .0484407
       unemployed_3 |  -.0216488   .0494541    -0.44   0.662    -.1187139    .0754162
           female_3 |  -.0698581   .0483026    -1.45   0.148    -.1646629    .0249468
              age_3 |  -.0009791   .0017939    -0.55   0.585    -.0044999    .0025418
              edu_3 |   .0102586   .0191856     0.53   0.593    -.0273976    .0479148
        religious_3 |   .0759033   .0571961     1.33   0.185    -.0363571    .1881638
              m5s_1 |   .1036889   .0630858     1.64   0.101    -.0201314    .2275092
               pd_1 |  -.0473182   .0580959    -0.81   0.416    -.1613447    .0667083
      eco_general_1 |   .0594564   .0280903     2.12   0.035     .0043228      .11459
            lr_no_1 |  -.0769557   .0769172    -1.00   0.317    -.2279233    .0740119
      polinterest_1 |   .2027743   .0432457     4.69   0.000     .1178948    .2876539
    talk_politics_1 |   .0783307   .0213362     3.67   0.000     .0364535     .120208
      pol_complex_1 |   .0001022     .02392     0.00   0.997    -.0468463    .0470507
internal_efficacy_1 |   .0449505   .0258093     1.74   0.082    -.0057062    .0956072
              _cons |   .5020677   .2342094     2.14   0.032      .042378    .9617574
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |       108           0         108     |
-----------------------------------------------------+
(est5 stored)
(3,752 missing values generated)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,037
Absorbing 1 HDFE group                            F(  15,    857) =       9.46
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1573
                                                  Adj R-squared   =     0.1036
                                                  Within R-sq.    =     0.0683
Number of clusters (comune_id) =        858       Root MSE        =     0.7185

                                   (Std. err. adjusted for 858 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
        web_event_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    web_event_4_web |   .1229126   .0378032     3.25   0.001     .0487148    .1971103
        eco_general |  -.0183994   .0254471    -0.72   0.470    -.0683453    .0315464
       unemployed_3 |  -.0308498   .0405854    -0.76   0.447    -.1105083    .0488088
           female_3 |   .0361602   .0354657     1.02   0.308    -.0334495      .10577
              age_3 |  -.0048315   .0013715    -3.52   0.000    -.0075233   -.0021396
              edu_3 |   .0186853   .0152171     1.23   0.220    -.0111817    .0485524
        religious_3 |   .1827608   .0433842     4.21   0.000      .097609    .2679125
              m5s_1 |    .027376   .0470042     0.58   0.560    -.0648807    .1196328
               pd_1 |  -.0991395   .0433946    -2.28   0.023    -.1843117   -.0139673
      eco_general_1 |   .1174036   .0245179     4.79   0.000     .0692813    .1655258
            lr_no_1 |  -.0918145   .0505933    -1.81   0.070    -.1911159    .0074869
      polinterest_1 |   .1397286   .0322192     4.34   0.000     .0764909    .2029664
    talk_politics_1 |   .0131715   .0157476     0.84   0.403    -.0177369    .0440798
      pol_complex_1 |   -.027978     .01845    -1.52   0.130    -.0641905    .0082345
internal_efficacy_1 |   .0359101   .0177154     2.03   0.043     .0011395    .0706808
              _cons |   .7467594   .1825521     4.09   0.000     .3884578    1.105061
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |       108           0         108     |
-----------------------------------------------------+
(est6 stored)

.         **estimate models for adjacent: 
.         foreach var of varlist web_politican_4-web_event_4 {
  2.         gen `var'_web=m5s_ref_neig_yn 
  3.         eststo: reghdfe `var' `var'_web `control_soc' `control_pol' [aweight=_webal] if m5s_ref_yn==0, absorb(province_1) clust
> er(comune_id)
  4.         drop `var'_web
  5.         }
(3,752 missing values generated)
(dropped 11 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        841
Absorbing 1 HDFE group                            F(  15,    585) =       2.62
Statistics robust to heteroskedasticity           Prob > F        =     0.0008
                                                  R-squared       =     0.1949
                                                  Adj R-squared   =     0.0786
                                                  Within R-sq.    =     0.0488
Number of clusters (comune_id) =        586       Root MSE        =     0.8083

                                   (Std. err. adjusted for 586 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
    web_politican_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
web_politican_4_web |   .0147594   .0699109     0.21   0.833    -.1225475    .1520663
        eco_general |  -.0061213   .0447122    -0.14   0.891    -.0939374    .0816947
       unemployed_3 |   .0090239   .0710952     0.13   0.899     -.130609    .1486567
           female_3 |  -.0107732   .0672943    -0.16   0.873     -.142941    .1213946
              age_3 |    .001597   .0024524     0.65   0.515    -.0032197    .0064136
              edu_3 |   .0286307   .0266229     1.08   0.283    -.0236574    .0809188
        religious_3 |   .2221512   .0715131     3.11   0.002     .0816974    .3626049
              m5s_1 |  -.0761345   .0843225    -0.90   0.367    -.2417462    .0894772
               pd_1 |  -.1116005   .0737238    -1.51   0.131     -.256396    .0331951
      eco_general_1 |   .0576983    .041994     1.37   0.170    -.0247791    .1401757
            lr_no_1 |  -.0799986   .1241594    -0.64   0.520     -.323851    .1638539
      polinterest_1 |    .148901   .0610668     2.44   0.015     .0289642    .2688379
    talk_politics_1 |   .0165152   .0240546     0.69   0.493    -.0307287     .063759
      pol_complex_1 |  -.0059767   .0353253    -0.17   0.866    -.0753565    .0634032
internal_efficacy_1 |   .0431536   .0385169     1.12   0.263    -.0324946    .1188018
              _cons |   .4715122   .3125363     1.51   0.132    -.1423177    1.085342
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |        92           0          92     |
-----------------------------------------------------+
(est7 stored)
(3,752 missing values generated)
(dropped 12 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        838
Absorbing 1 HDFE group                            F(  15,    581) =       3.35
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.2114
                                                  Adj R-squared   =     0.0983
                                                  Within R-sq.    =     0.0639
Number of clusters (comune_id) =        582       Root MSE        =     0.9381

                                   (Std. err. adjusted for 582 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
     web_socmedia_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
 web_socmedia_4_web |   .1035821   .0810935     1.28   0.202      -.05569    .2628542
        eco_general |   .0790241   .0535152     1.48   0.140    -.0260827     .184131
       unemployed_3 |   .0158837    .083821     0.19   0.850    -.1487455    .1805128
           female_3 |  -.0148491    .081685    -0.18   0.856    -.1752829    .1455848
              age_3 |   -.004655   .0027867    -1.67   0.095    -.0101283    .0008183
              edu_3 |   .0203421   .0305715     0.67   0.506     -.039702    .0803862
        religious_3 |   .2949147   .0845224     3.49   0.001      .128908    .4609215
              m5s_1 |  -.0196415   .1032735    -0.19   0.849    -.2224765    .1831935
               pd_1 |  -.1997089   .0830599    -2.40   0.017    -.3628432   -.0365746
      eco_general_1 |  -.0333896   .0449007    -0.74   0.457     -.121577    .0547979
            lr_no_1 |  -.0743098   .1315999    -0.56   0.573    -.3327793    .1841598
      polinterest_1 |    .114177   .0643545     1.77   0.077    -.0122189    .2405729
    talk_politics_1 |   .0788362   .0293427     2.69   0.007     .0212055    .1364668
      pol_complex_1 |  -.0176062   .0396711    -0.44   0.657    -.0955225    .0603102
internal_efficacy_1 |   .0665322   .0395417     1.68   0.093      -.01113    .1441943
              _cons |   .6915342   .3272624     2.11   0.035     .0487727    1.334296
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |        91           0          91     |
-----------------------------------------------------+
(est8 stored)
(3,752 missing values generated)
(dropped 11 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        839
Absorbing 1 HDFE group                            F(  15,    582) =       3.89
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1760
                                                  Adj R-squared   =     0.0566
                                                  Within R-sq.    =     0.0736
Number of clusters (comune_id) =        583       Root MSE        =     0.8966

                                   (Std. err. adjusted for 583 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
        web_video_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    web_video_4_web |   .0976148   .0797714     1.22   0.222    -.0590601    .2542897
        eco_general |  -.0058511   .0466648    -0.13   0.900     -.097503    .0858009
       unemployed_3 |  -.0540344   .0736067    -0.73   0.463    -.1986015    .0905326
           female_3 |   .0873433   .0768848     1.14   0.256    -.0636621    .2383487
              age_3 |    .003441   .0027035     1.27   0.204    -.0018689    .0087509
              edu_3 |   .0882685   .0282243     3.13   0.002     .0328345    .1437025
        religious_3 |   .1490124   .0843195     1.77   0.078    -.0165951      .31462
              m5s_1 |   .1267542   .0894333     1.42   0.157    -.0488971    .3024055
               pd_1 |   .0796241   .0927554     0.86   0.391    -.1025519    .2618002
      eco_general_1 |   .0624272   .0503462     1.24   0.215    -.0364552    .1613096
            lr_no_1 |   .0096649   .1056352     0.09   0.927    -.1978077    .2171374
      polinterest_1 |   .0571424   .0643019     0.89   0.375    -.0691497    .1834345
    talk_politics_1 |   .0947613   .0278767     3.40   0.001     .0400102    .1495123
      pol_complex_1 |   .0274928   .0423932     0.65   0.517    -.0557694    .1107551
internal_efficacy_1 |   .0381589   .0410903     0.93   0.353    -.0425444    .1188621
              _cons |   .0935499   .3111066     0.30   0.764    -.5174785    .7045782
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |        92           0          92     |
-----------------------------------------------------+
(est9 stored)
(3,752 missing values generated)
(dropped 11 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        842
Absorbing 1 HDFE group                            F(  15,    583) =       5.34
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.2004
                                                  Adj R-squared   =     0.0851
                                                  Within R-sq.    =     0.0932
Number of clusters (comune_id) =        584       Root MSE        =     0.9520

                                   (Std. err. adjusted for 584 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
        web_share_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    web_share_4_web |   .0171943   .0814958     0.21   0.833    -.1428668    .1772554
        eco_general |  -.0609579   .0542951    -1.12   0.262    -.1675957    .0456799
       unemployed_3 |   .0314018   .0831151     0.38   0.706    -.1318397    .1946433
           female_3 |  -.1042831   .0813366    -1.28   0.200    -.2640316    .0554654
              age_3 |   .0068724   .0028121     2.44   0.015     .0013494    .0123954
              edu_3 |   .0803594   .0308567     2.60   0.009     .0197556    .1409633
        religious_3 |   .2582979   .1006258     2.57   0.011     .0606647     .455931
              m5s_1 |   .0704148   .1039233     0.68   0.498    -.1336948    .2745245
               pd_1 |    .013554   .0932464     0.15   0.884    -.1695858    .1966939
      eco_general_1 |   .0779993   .0534366     1.46   0.145    -.0269525     .182951
            lr_no_1 |   .0941704   .1357617     0.69   0.488    -.1724712     .360812
      polinterest_1 |   .1327547    .070525     1.88   0.060    -.0057593    .2712686
    talk_politics_1 |   .1045974   .0287135     3.64   0.000     .0482028    .1609919
      pol_complex_1 |   .0060448    .042366     0.14   0.887    -.0771637    .0892534
internal_efficacy_1 |   .0198593   .0434833     0.46   0.648    -.0655436    .1052622
              _cons |  -.1145366   .3222241    -0.36   0.722    -.7473981    .5183249
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |        92           0          92     |
-----------------------------------------------------+
(est10 stored)
(3,752 missing values generated)
(dropped 12 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        835
Absorbing 1 HDFE group                            F(  15,    578) =       3.56
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.2398
                                                  Adj R-squared   =     0.1303
                                                  Within R-sq.    =     0.0791
Number of clusters (comune_id) =        579       Root MSE        =     0.8867

                                   (Std. err. adjusted for 579 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
      web_discuss_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
  web_discuss_4_web |   .0403562   .0810097     0.50   0.619     -.118753    .1994655
        eco_general |   .0496421   .0501443     0.99   0.323    -.0488451    .1481294
       unemployed_3 |   .0399343   .0774013     0.52   0.606    -.1120877    .1919564
           female_3 |  -.0263923   .0785805    -0.34   0.737    -.1807305    .1279459
              age_3 |   .0030131   .0027174     1.11   0.268    -.0023241    .0083502
              edu_3 |   .0569754   .0290318     1.96   0.050    -.0000452    .1139961
        religious_3 |   .1340102   .0841683     1.59   0.112    -.0313027    .2993231
              m5s_1 |   .1029256   .1019816     1.01   0.313    -.0973741    .3032254
               pd_1 |  -.0641927   .0850318    -0.75   0.451    -.2312016    .1028162
      eco_general_1 |   .0378924    .043292     0.88   0.382    -.0471365    .1229212
            lr_no_1 |   .0165712   .1315433     0.13   0.900      -.24179    .2749324
      polinterest_1 |   .0890028   .0665948     1.34   0.182    -.0417945    .2198001
    talk_politics_1 |   .1066793   .0293731     3.63   0.000     .0489884    .1643703
      pol_complex_1 |    .028443   .0383344     0.74   0.458    -.0468487    .1037348
internal_efficacy_1 |   .0518444   .0405937     1.28   0.202    -.0278847    .1315735
              _cons |  -.0395133   .3342632    -0.12   0.906    -.6960318    .6170053
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |        91           0          91     |
-----------------------------------------------------+
(est11 stored)
(3,752 missing values generated)
(dropped 11 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        834
Absorbing 1 HDFE group                            F(  15,    579) =       3.14
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.2023
                                                  Adj R-squared   =     0.0860
                                                  Within R-sq.    =     0.0681
Number of clusters (comune_id) =        580       Root MSE        =     0.6747

                                   (Std. err. adjusted for 580 clusters in comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
        web_event_4 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    web_event_4_web |   .0875251   .0571303     1.53   0.126    -.0246828     .199733
        eco_general |  -.0020957   .0394602    -0.05   0.958    -.0795982    .0754068
       unemployed_3 |  -.0413166   .0613802    -0.67   0.501    -.1618715    .0792383
           female_3 |   .0502805   .0576483     0.87   0.383    -.0629447    .1635057
              age_3 |  -.0018751    .002042    -0.92   0.359    -.0058858    .0021356
              edu_3 |   .0549371    .020704     2.65   0.008      .014273    .0956012
        religious_3 |   .1652146   .0584614     2.83   0.005     .0503923    .2800369
              m5s_1 |   .0299624   .0693416     0.43   0.666    -.1062293     .166154
               pd_1 |  -.1022624   .0669708    -1.53   0.127    -.2337977    .0292728
      eco_general_1 |   .1008141   .0374701     2.69   0.007     .0272203    .1744079
            lr_no_1 |   -.082436   .0852037    -0.97   0.334     -.249782      .08491
      polinterest_1 |   .0758089    .049416     1.53   0.126    -.0212476    .1728654
    talk_politics_1 |   .0305978   .0235442     1.30   0.194    -.0156446    .0768403
      pol_complex_1 |  -.0326634   .0286345    -1.14   0.254    -.0889036    .0235768
internal_efficacy_1 |   .0419438    .029105     1.44   0.150    -.0152205    .0991081
              _cons |   .4873613   .2593305     1.88   0.061    -.0219818    .9967044
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  province_1 |        92           0          92     |
-----------------------------------------------------+
(est12 stored)

.         ** coefplot: 
.         coefplot ///
>         (est6, offset(0) ciopts(lwidth(0.6 ..)) msymbol(d) msize(medlarge) mlwidth(medthick)) ///
>         (est1, offset(0) pstyle(p3)) ///
>         (est2, offset(0) pstyle(p3)) ///
>         (est3, offset(0) pstyle(p3)) ///
>         (est4, offset(0) pstyle(p3)) ///
>         (est5, offset(0) pstyle(p3)) ///
>         , legend(off) bylabel("{it:M5S active locally}") xtitle("{it:from less to more frequently used} (1-4)") ///
>         || ///
>         (est12, offset(0)) ///
>         (est7, offset(0)) ///
>         (est8, offset(0)) ///
>         (est9, offset(0)) ///
>         (est10, offset(0)) ///
>         (est11, offset(0)) ///
>         , ///
>         keep(*_contact *_web) level(95) title("{bf:B) information-seeking}") legend(off) xline(0) ///
>         bylabel("{it: M5S active in neighboring municipality}") byopts(legend(off))  ///
>         xtitle("{it:from less to more frequently used} (1-4)") ///
>         ciopts(lwidth(0.4 ..)) mlwidth(medthick) msize(medsmall) mfcolor(white) ///
>         groups(*_contact = "{bf: information}" *_web = "{bf: web}", angle(rvertical)) ///
>         grid(none) ///
>         yscale(alt axis(1)) ///
>         ylabel( ///
>         1 "{bf:offline event organized via web}" /// 
>         2 "social media profile of party" ///
>         3 "videos on campaigns" ///
>         4 "shared contents about campaign" /// 
>         5 "debated campaign online" /// 
>         6 "website of politician" ///
>         ) 

.         ** save as pdf: 
.         graph export "$figures/fig6b_manipulation.pdf", replace
file /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/results/figures/fig6b_manipulation.pdf saved as PDF format

.         
.         // in-partisan effect (fig 7b):
.         ** estimate: 
.         reghdfe referendum_no_wunsure c.m5s_wn_wave_157##i.m5s_1 eco_general unemployed_3 female_3 age_3 edu_3 religious_3 pd_1 pd
> l_1 lr_no_1 polinterest_1 talk_politics_1 eco_general_1 pol_complex_1 internal_efficacy_1, absorb(comune_id post) cluster(id comun
> e_id)
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(  17,    855) =      44.92
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4582
                                                  Adj R-squared   =     0.3100
Number of clusters (id)      =      2,064         Within R-sq.    =     0.1521
Number of clusters (comune_id) =        856       Root MSE        =     0.4149

                                    (Std. err. adjusted for 856 clusters in id comune_id)
-----------------------------------------------------------------------------------------
                        |               Robust
  referendum_no_wunsure | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
        m5s_wn_wave_157 |   .0637802   .0275121     2.32   0.021     .0097811    .1177794
                1.m5s_1 |   .0626804   .0392123     1.60   0.110    -.0142834    .1396441
                        |
m5s_1#c.m5s_wn_wave_157 |
                     1  |   .0429711   .0222967     1.93   0.054    -.0007915    .0867337
                        |
            eco_general |  -.1656142   .0127749   -12.96   0.000     -.190688   -.1405405
           unemployed_3 |  -.0313732   .0236138    -1.33   0.184    -.0777209    .0149746
               female_3 |  -.0130891   .0216023    -0.61   0.545    -.0554889    .0293107
                  age_3 |  -.0013234   .0007516    -1.76   0.079    -.0027986    .0001518
                  edu_3 |  -.0133026   .0099103    -1.34   0.180     -.032754    .0061489
            religious_3 |  -.0533197   .0279444    -1.91   0.057    -.1081673    .0015279
                   pd_1 |  -.0852797   .0285624    -2.99   0.003    -.1413403   -.0292191
                  pdl_1 |   .1359632   .0370733     3.67   0.000     .0631977    .2087286
                lr_no_1 |   .0164254   .0365264     0.45   0.653    -.0552664    .0881173
          polinterest_1 |   .0404911   .0196434     2.06   0.040     .0019362    .0790461
        talk_politics_1 |   .0245914   .0089721     2.74   0.006     .0069815    .0422014
          eco_general_1 |  -.0087411   .0134252    -0.65   0.515    -.0350913    .0176091
          pol_complex_1 |   .0041697   .0117279     0.36   0.722    -.0188491    .0271885
    internal_efficacy_1 |   .0124975   .0111132     1.12   0.261    -.0093148    .0343098
                  _cons |   .7718135    .104575     7.38   0.000     .5665597    .9770672
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         ** calculate margins: 
.         margins, dydx(m5s_1) at(m5s_wn_wave_157=(0(.1)5))

Average marginal effects                                 Number of obs = 4,070
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  1.m5s_1
1._at:  m5s_wn_wave_157 =   0
2._at:  m5s_wn_wave_157 =  .1
3._at:  m5s_wn_wave_157 =  .2
4._at:  m5s_wn_wave_157 =  .3
5._at:  m5s_wn_wave_157 =  .4
6._at:  m5s_wn_wave_157 =  .5
7._at:  m5s_wn_wave_157 =  .6
8._at:  m5s_wn_wave_157 =  .7
9._at:  m5s_wn_wave_157 =  .8
10._at: m5s_wn_wave_157 =  .9
11._at: m5s_wn_wave_157 =   1
12._at: m5s_wn_wave_157 = 1.1
13._at: m5s_wn_wave_157 = 1.2
14._at: m5s_wn_wave_157 = 1.3
15._at: m5s_wn_wave_157 = 1.4
16._at: m5s_wn_wave_157 = 1.5
17._at: m5s_wn_wave_157 = 1.6
18._at: m5s_wn_wave_157 = 1.7
19._at: m5s_wn_wave_157 = 1.8
20._at: m5s_wn_wave_157 = 1.9
21._at: m5s_wn_wave_157 =   2
22._at: m5s_wn_wave_157 = 2.1
23._at: m5s_wn_wave_157 = 2.2
24._at: m5s_wn_wave_157 = 2.3
25._at: m5s_wn_wave_157 = 2.4
26._at: m5s_wn_wave_157 = 2.5
27._at: m5s_wn_wave_157 = 2.6
28._at: m5s_wn_wave_157 = 2.7
29._at: m5s_wn_wave_157 = 2.8
30._at: m5s_wn_wave_157 = 2.9
31._at: m5s_wn_wave_157 =   3
32._at: m5s_wn_wave_157 = 3.1
33._at: m5s_wn_wave_157 = 3.2
34._at: m5s_wn_wave_157 = 3.3
35._at: m5s_wn_wave_157 = 3.4
36._at: m5s_wn_wave_157 = 3.5
37._at: m5s_wn_wave_157 = 3.6
38._at: m5s_wn_wave_157 = 3.7
39._at: m5s_wn_wave_157 = 3.8
40._at: m5s_wn_wave_157 = 3.9
41._at: m5s_wn_wave_157 =   4
42._at: m5s_wn_wave_157 = 4.1
43._at: m5s_wn_wave_157 = 4.2
44._at: m5s_wn_wave_157 = 4.3
45._at: m5s_wn_wave_157 = 4.4
46._at: m5s_wn_wave_157 = 4.5
47._at: m5s_wn_wave_157 = 4.6
48._at: m5s_wn_wave_157 = 4.7
49._at: m5s_wn_wave_157 = 4.8
50._at: m5s_wn_wave_157 = 4.9
51._at: m5s_wn_wave_157 =   5

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0.m5s_1      |  (base outcome)
-------------+----------------------------------------------------------------
1.m5s_1      |
         _at |
          1  |   .0626804   .0392123     1.60   0.110    -.0141744    .1395351
          2  |   .0669775   .0380744     1.76   0.079    -.0076469    .1416019
          3  |   .0712746   .0370358     1.92   0.054    -.0013143    .1438634
          4  |   .0755717   .0361052     2.09   0.036     .0048067    .1463367
          5  |   .0798688   .0352912     2.26   0.024     .0106993    .1490382
          6  |   .0841659   .0346019     2.43   0.015     .0163475    .1519843
          7  |    .088463   .0340449     2.60   0.009     .0217363    .1551898
          8  |   .0927601   .0336268     2.76   0.006     .0268528    .1586675
          9  |   .0970572   .0333529     2.91   0.004     .0316868    .1624276
         10  |   .1013543   .0332266     3.05   0.002     .0362314    .1664773
         11  |   .1056515   .0332497     3.18   0.001     .0404832    .1708197
         12  |   .1099486   .0334219     3.29   0.001     .0444429    .1754542
         13  |   .1142457   .0337408     3.39   0.001     .0481149    .1803765
         14  |   .1185428   .0342025     3.47   0.001     .0515072    .1855784
         15  |   .1228399   .0348011     3.53   0.000      .054631    .1910488
         16  |    .127137   .0355298     3.58   0.000     .0574999    .1967741
         17  |   .1314341   .0363808     3.61   0.000     .0601291    .2027391
         18  |   .1357312   .0373456     3.63   0.000     .0625352    .2089273
         19  |   .1400283   .0384158     3.65   0.000     .0647347    .2153219
         20  |   .1443254   .0395828     3.65   0.000     .0667446    .2219063
         21  |   .1486226   .0408382     3.64   0.000     .0685811     .228664
         22  |   .1529197   .0421743     3.63   0.000     .0702597    .2355797
         23  |   .1572168   .0435835     3.61   0.000     .0717948    .2426388
         24  |   .1615139    .045059     3.58   0.000     .0731999    .2498278
         25  |    .165811   .0465945     3.56   0.000     .0744875    .2571345
         26  |   .1701081   .0481843     3.53   0.000     .0756687    .2645476
         27  |   .1744052   .0498231     3.50   0.000     .0767536    .2720568
         28  |   .1787023   .0515064     3.47   0.001     .0777516     .279653
         29  |   .1829994   .0532298     3.44   0.001     .0786709     .287328
         30  |   .1872966   .0549897     3.41   0.001     .0795187    .2950744
         31  |   .1915937   .0567826     3.37   0.001     .0803019    .3028854
         32  |   .1958908   .0586054     3.34   0.001     .0810263    .3107553
         33  |   .2001879   .0604555     3.31   0.001     .0816972    .3186786
         34  |    .204485   .0623305     3.28   0.001     .0823194    .3266506
         35  |   .2087821   .0642282     3.25   0.001     .0828972     .334667
         36  |   .2130792   .0661465     3.22   0.001     .0834344     .342724
         37  |   .2173763   .0680839     3.19   0.001     .0839344    .3508183
         38  |   .2216734   .0700386     3.17   0.002     .0844003    .3589466
         39  |   .2259705   .0720093     3.14   0.002     .0848349    .3671062
         40  |   .2302677   .0739947     3.11   0.002     .0852407    .3752947
         41  |   .2345648   .0759937     3.09   0.002     .0856199    .3835096
         42  |   .2388619   .0780051     3.06   0.002     .0859746    .3917491
         43  |    .243159   .0800282     3.04   0.002     .0863066    .4000113
         44  |   .2474561   .0820619     3.02   0.003     .0866177    .4082945
         45  |   .2517532   .0841056     2.99   0.003     .0869093    .4165971
         46  |   .2560503   .0861585     2.97   0.003     .0871828    .4249179
         47  |   .2603474     .08822     2.95   0.003     .0874395    .4332554
         48  |   .2646445   .0902894     2.93   0.003     .0876805    .4416086
         49  |   .2689417   .0923664     2.91   0.004     .0879069    .4499764
         50  |   .2732388   .0944503     2.89   0.004     .0881196    .4583579
         51  |   .2775359   .0965407     2.87   0.004     .0883196    .4667521
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.         ** produce figure:
.         marginsplot, ///
>         yline(0) ///
>         xtitle("M5S activity (cont.)") ///
>         ytitle("Marginal effects on {bf:Referendum: No}") ///
>         title("{bf: B) in-partisan}") ///
>         recast(scatter) recastci(rspike) ///
>         ciopts(col(uzhblue%50)) ///
>         plotopts(msymbol(O) mlcolor(uzhblue) mfcolor(white))

Variables that uniquely identify margins: m5s_wn_wave_157

.         ** save as pdf: 
.         graph export "$figures/fig7b_partisan.pdf", replace
file /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/results/figures/fig7b_partisan.pdf saved as PDF format

.         
. 
. 
. 
. 
. ***SUPPORTING INFORMATION***
.         
.         use "$data_coded/placebased_individual.dta", clear 

.         xtset 

Panel variable: id (strongly balanced)
 Time variable: wave, 1 to 4, but with gaps
         Delta: 1 unit

. 
.         // define locals: 
.         local control_soc eco_general unemployed_3 female_3 age_3 edu_3 religious_3 

.         local control_pol m5s_1 pd_1 eco_general_1 lr_no_1 polinterest_1 talk_politics_1 pol_complex_1 internal_efficacy_1

. 
.         // report balance figure (fig a9)
.         ** standardize:
.         standard2 polinterest_1 talk_politics_1 female_1 age_1 edu_1 unemployed_1 pol_complex_1 internal_efficacy_1 eco_general_1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std_polint~1 |      8,067   -1.71e-08           1  -2.604563   1.274154

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std2_polin~1 |      8,067   -8.57e-09          .5  -1.302281   .6370769

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
mc_polinte~1 |      8,067    1.04e-08    .7734517  -2.014503   .9854965

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
log_polint~1 |      8,067    1.368849    .2131374   .6931472   1.609438

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std_talk_p~1 |      8,001    1.81e-09           1  -2.267556   .9688625

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std2_talk_~1 |      8,001    9.05e-10          .5  -1.133778   .4844312

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
mc_talk_po~1 |      8,001    1.01e-08    1.544918  -3.503187   1.496813

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
log_talk_p~1 |      8,001    1.655222    .3387789   .6931472    1.94591

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std_female_1 |      8,181   -1.74e-08           1   -.923297   1.082943

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std2_femal~1 |      8,181   -8.70e-09          .5  -.4616485   .5414714

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 mc_female_1 |      8,181   -3.50e-09    .4984449  -.4602127   .5397873

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
log_female_1 |      8,181    .3189951    .3454957          0   .6931472

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   std_age_1 |      8,181    3.00e-09           1  -1.907793   2.420617

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  std2_age_1 |      8,181    1.50e-09          .5  -.9538966   1.210308

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    mc_age_1 |      8,181   -3.37e-07    15.24809  -29.09021   36.90979

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   log_age_1 |      8,181     3.84115     .332654   2.995732   4.454347

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   std_edu_1 |      8,181    1.59e-08           1  -3.032929    1.28227

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  std2_edu_1 |      8,181    7.97e-09          .5  -1.516465   .6411349

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    mc_edu_1 |      8,181    1.69e-09    .6952171  -2.108544   .8914558

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   log_edu_1 |      8,181    1.396989    .1858022   .6931472   1.609438

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std_unempl~1 |      8,181   -1.49e-09           1  -.3301122   3.028903

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std2_unemp~1 |      8,181   -7.43e-10          .5  -.1650561   1.514451

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
mc_unemplo~1 |      8,181   -1.62e-09    .2977063  -.0982765   .9017235

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
log_unempl~1 |      8,181    .0681201    .2063543          0   .6931472

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std_pol_co~1 |      7,998    1.31e-08           1  -1.352369   1.661353

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std2_pol_c~1 |      7,998    6.53e-09          .5  -.6761847   .8306766

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
mc_pol_com~1 |      7,998    2.68e-10    .9954467  -1.346212   1.653788

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
log_pol_co~1 |      7,998     1.16164    .3083615   .6931472   1.609438

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std_intern~1 |      7,986   -1.58e-08           1   -1.09157    1.95111

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std2_inter~1 |      7,986   -7.90e-09          .5   -.545785   .9755549

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
mc_interna~1 |      7,986    8.78e-09    .9859729  -1.076258   1.923742

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
log_intern~1 |      7,986    1.073116    .3177424   .6931472   1.609438

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std_eco_ge~1 |      8,127    1.29e-08           1  -.9824372   3.690824

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
std2_eco_g~1 |      8,127    6.47e-09          .5  -.4912186   1.845412

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
mc_eco_gen~1 |      8,127   -6.88e-10    .8559333  -.8409007   3.159099

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
log_eco_ge~1 |      8,127    1.002293    .2848381   .6931472   1.791759

.         ** estimate:
.         ebalance m5s_ever `control_soc' `control_pol'


Data Setup
Treatment variable:   m5s_ever
Covariate adjustment: eco_general unemployed_3 female_3 age_3 edu_3 religious_3 m5s_1 pd_1 eco_general_1 lr_no_1 polinterest_1 talk_
> politics_1 pol_complex_1 internal_efficacy_1 

Optimizing...
Iteration 1: Max Difference = 11441.4483
Iteration 2: Max Difference = 4207.68638
Iteration 3: Max Difference = 1546.53494
Iteration 4: Max Difference = 567.554361
Iteration 5: Max Difference = 207.41382
Iteration 6: Max Difference = 74.9425038
Iteration 7: Max Difference = 26.2543719
Iteration 8: Max Difference = 8.46000096
Iteration 9: Max Difference = 2.185187
Iteration 10: Max Difference = .332235182
Iteration 11: Max Difference = .01710886
Iteration 12: Max Difference = .000080297
maximum difference smaller than the tolerance level; convergence achieved


Treated units: 4018    total of weights: 4018
Control units: 2233    total of weights: 4018


Before: without weighting

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |     2.351      .8945      .1253 
unemployed_3 |     .4567      .2482      .1739 |     .4971      .2501     .01164 
    female_3 |     .4216      .2439      .3175 |     .4339      .2457      .2666 
       age_3 |     52.52      234.2    -.03322 |     51.26      231.4     .05314 
       edu_3 |     5.183      1.754     -1.227 |     4.815      2.236     -.9771 
 religious_3 |     .7857      .1684     -1.393 |     .8186      .1485     -1.654 
       m5s_1 |     .2165      .1697      1.377 |     .2262      .1751      1.309 
        pd_1 |     .2805      .2019      .9773 |     .2642      .1945       1.07 
eco_genera~1 |     1.895      .7781      .8958 |      1.83      .6965      .9648 
     lr_no_1 |     .1147      .1016      2.418 |     .1303      .1134      2.196 
polinteres~1 |     3.179      .4515     -.3309 |     3.109      .4841     -.4292 
talk_polit~1 |     4.716      2.031      -.768 |     4.679      2.099      -.703 
pol_comple~1 |     2.434      .9562       .118 |     2.338      .9988      .2854 
internal_e~1 |     2.153       .988      .4234 |      2.18      .9894      .3298 


After:  _webal as the weighting variable

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |      2.41      .8877      .0831 
unemployed_3 |     .4567      .2482      .1739 |     .4567      .2482      .1739 
    female_3 |     .4216      .2439      .3175 |     .4216       .244      .3175 
       age_3 |     52.52      234.2    -.03322 |     52.52      228.6     -.0179 
       edu_3 |     5.183      1.754     -1.227 |     5.183      1.652     -1.291 
 religious_3 |     .7857      .1684     -1.393 |     .7857      .1684     -1.393 
       m5s_1 |     .2165      .1697      1.377 |     .2165      .1697      1.377 
        pd_1 |     .2805      .2019      .9773 |     .2805      .2019      .9773 
eco_genera~1 |     1.895      .7781      .8958 |     1.895      .7354      .9299 
     lr_no_1 |     .1147      .1016      2.418 |     .1147      .1016      2.418 
polinteres~1 |     3.179      .4515     -.3309 |     3.179      .4567     -.4357 
talk_polit~1 |     4.716      2.031      -.768 |     4.716      2.013      -.709 
pol_comple~1 |     2.434      .9562       .118 |     2.434       1.02      .1746 
internal_e~1 |     2.153       .988      .4234 |     2.153      .9738      .3348 

.         eststo clear 

.         foreach var of varlist female_1 std_age_1 std_edu_1 unemployed_1 m5s_1 pd_1 lr_no_1 std_polinterest_1 std_talk_politics_1 
> std_pol_complex_1 std_internal_efficacy_1 std_eco_general_1 {
  2.                 rename  m5s_ever `var'_treat
  3.                 eststo raw_`var': reg `var' `var'_treat if wave==1
  4.                 rename  `var'_treat m5s_ever 
  5.         }

      Source |       SS           df       MS      Number of obs   =     2,699
-------------+----------------------------------   F(1, 2697)      =      0.02
       Model |  .004283619         1  .004283619   Prob > F        =    0.8956
    Residual |  670.384008     2,697  .248566559   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0004
       Total |  670.388292     2,698  .248476016   Root MSE        =    .49856

--------------------------------------------------------------------------------
      female_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
female_1_treat |  -.0026163   .0199299    -0.13   0.896    -.0416958    .0364632
         _cons |   .4614604   .0158775    29.06   0.000     .4303271    .4925938
--------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,699
-------------+----------------------------------   F(1, 2697)      =      4.87
       Model |  4.85244732         1  4.85244732   Prob > F        =    0.0275
    Residual |  2689.44695     2,697  .997199463   R-squared       =    0.0018
-------------+----------------------------------   Adj R-squared   =    0.0014
       Total |   2694.2994     2,698  .998628391   Root MSE        =     .9986

---------------------------------------------------------------------------------
      std_age_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
std_age_1_treat |   .0880572   .0399186     2.21   0.027      .009783    .1663313
          _cons |  -.0567986   .0318019    -1.79   0.074    -.1191571    .0055598
---------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,699
-------------+----------------------------------   F(1, 2697)      =     34.84
       Model |  33.7648078         1  33.7648078   Prob > F        =    0.0000
    Residual |    2614.049     2,697  .969243233   R-squared       =    0.0128
-------------+----------------------------------   Adj R-squared   =    0.0124
       Total |  2647.81381     2,698  .981398742   Root MSE        =     .9845

---------------------------------------------------------------------------------
      std_edu_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
std_edu_1_treat |   .2322825   .0393551     5.90   0.000     .1551133    .3094516
          _cons |  -.1415417   .0313529    -4.51   0.000    -.2030199   -.0800635
---------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,699
-------------+----------------------------------   F(1, 2697)      =      1.14
       Model |  .100252909         1  .100252909   Prob > F        =    0.2858
    Residual |  237.272107     2,697   .08797631   R-squared       =    0.0004
-------------+----------------------------------   Adj R-squared   =    0.0001
       Total |   237.37236     2,698   .08798086   Root MSE        =    .29661

------------------------------------------------------------------------------------
      unemployed_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
unemployed_1_treat |  -.0126571   .0118568    -1.07   0.286    -.0359064    .0105923
             _cons |   .1054767   .0094459    11.17   0.000     .0869547    .1239987
------------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,136
-------------+----------------------------------   F(1, 2134)      =      0.29
       Model |  .049168003         1  .049168003   Prob > F        =    0.5924
    Residual |  365.972836     2,134  .171496174   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0003
       Total |  366.022004     2,135  .171438878   Root MSE        =    .41412

------------------------------------------------------------------------------
       m5s_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 m5s_1_treat |  -.0100184   .0187104    -0.54   0.592     -.046711    .0266742
       _cons |   .2260184   .0150119    15.06   0.000      .196579    .2554578
------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,136
-------------+----------------------------------   F(1, 2134)      =      0.67
       Model |  .132733159         1  .132733159   Prob > F        =    0.4139
    Residual |  424.196855     2,134  .198780157   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  424.329588     2,135  .198749222   Root MSE        =    .44585

------------------------------------------------------------------------------
        pd_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  pd_1_treat |   .0164606   .0201439     0.82   0.414    -.0230431    .0559644
       _cons |   .2628121    .016162    16.26   0.000     .2311172    .2945069
------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,699
-------------+----------------------------------   F(1, 2697)      =      2.00
       Model |  .290056264         1  .290056264   Prob > F        =    0.1574
    Residual |  391.114168     2,697   .14501823   R-squared       =    0.0007
-------------+----------------------------------   Adj R-squared   =    0.0004
       Total |  391.404224     2,698  .145071988   Root MSE        =    .38081

-------------------------------------------------------------------------------
      lr_no_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
lr_no_1_treat |  -.0215291   .0152228    -1.41   0.157    -.0513787    .0083205
        _cons |   .1896552   .0121275    15.64   0.000      .165875    .2134354
-------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,669
-------------+----------------------------------   F(1, 2667)      =      5.70
       Model |  5.66904443         1  5.66904443   Prob > F        =    0.0171
    Residual |   2654.2005     2,667  .995200786   R-squared       =    0.0021
-------------+----------------------------------   Adj R-squared   =    0.0018
       Total |  2659.86954     2,668  .996952602   Root MSE        =     .9976

-----------------------------------------------------------------------------------------
      std_polinterest_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
std_polinterest_1_treat |    .095692   .0400937     2.39   0.017     .0170741      .17431
                  _cons |   -.057168   .0319323    -1.79   0.074    -.1197826    .0054466
-----------------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,648
-------------+----------------------------------   F(1, 2646)      =      0.31
       Model |  .310821271         1  .310821271   Prob > F        =    0.5770
    Residual |  2642.54987     2,646  .998696096   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0003
       Total |  2642.86069     2,647  .998436227   Root MSE        =    .99935

-------------------------------------------------------------------------------------------
      std_talk_politics_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
std_talk_politics_1_treat |   .0225117   .0403525     0.56   0.577    -.0566138    .1016373
                    _cons |  -.0124597   .0321701    -0.39   0.699    -.0755409    .0506214
-------------------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,646
-------------+----------------------------------   F(1, 2644)      =      6.53
       Model |  6.49770255         1  6.49770255   Prob > F        =    0.0107
    Residual |  2631.28486     2,644  .995190947   R-squared       =    0.0025
-------------+----------------------------------   Adj R-squared   =    0.0021
       Total |  2637.78257     2,645  .997271292   Root MSE        =    .99759

-----------------------------------------------------------------------------------------
      std_pol_complex_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
std_pol_complex_1_treat |   .1029049   .0402726     2.56   0.011      .023936    .1818739
                  _cons |  -.0641874   .0320804    -2.00   0.046    -.1270926   -.0012821
-----------------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,643
-------------+----------------------------------   F(1, 2641)      =      0.10
       Model |  .095418752         1  .095418752   Prob > F        =    0.7568
    Residual |   2628.0237     2,641  .995086595   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0003
       Total |  2628.11911     2,642  .994746069   Root MSE        =    .99754

-----------------------------------------------------------------------------------------------
      std_internal_efficacy_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------------+----------------------------------------------------------------
std_internal_efficacy_1_treat |    .012477   .0402925     0.31   0.757     -.066531     .091485
                        _cons |  -.0111982   .0320953    -0.35   0.727    -.0741327    .0517364
-----------------------------------------------------------------------------------------------

      Source |       SS           df       MS      Number of obs   =     2,687
-------------+----------------------------------   F(1, 2685)      =      4.42
       Model |   4.3875586         1   4.3875586   Prob > F        =    0.0357
    Residual |  2667.14601     2,685   .99335047   R-squared       =    0.0016
-------------+----------------------------------   Adj R-squared   =    0.0013
       Total |  2671.53357     2,686  .994614137   Root MSE        =    .99667

-----------------------------------------------------------------------------------------
      std_eco_general_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
std_eco_general_1_treat |   .0839126    .039927     2.10   0.036     .0056218    .1622035
                  _cons |  -.0556371    .031805    -1.75   0.080    -.1180019    .0067277
-----------------------------------------------------------------------------------------

.         foreach var of varlist female_1 std_age_1 std_edu_1 unemployed_1 m5s_1 pd_1 lr_no_1 std_polinterest_1 std_talk_politics_1 
> std_pol_complex_1 std_internal_efficacy_1 std_eco_general_1 { 
  2.                 rename  m5s_ever `var'_treat
  3.                 eststo wgt_`var': reg `var' `var'_treat [aweight=_webal] if wave==1
  4.                 rename  `var'_treat m5s_ever 
  5.         }
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.00
       Model |  7.6246e-07         1  7.6246e-07   Prob > F        =    0.9986
    Residual |   509.69798     2,087  .244225194   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  509.697981     2,088  .244108228   Root MSE        =    .49419

--------------------------------------------------------------------------------
      female_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
female_1_treat |   .0000382   .0216251     0.00   0.999    -.0423708    .0424472
         _cons |   .4224655   .0153122    27.59   0.000     .3924368    .4524942
--------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.01
       Model |  .005481039         1  .005481039   Prob > F        =    0.9409
    Residual |  2079.46808     2,087  .996391032   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  2079.47357     2,088  .995916458   Root MSE        =    .99819

---------------------------------------------------------------------------------
      std_age_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
std_age_1_treat |   .0032396   .0436795     0.07   0.941    -.0824202    .0888995
          _cons |   .0909765   .0309283     2.94   0.003      .030323    .1516301
---------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.69
       Model |  .624326392         1  .624326392   Prob > F        =    0.4054
    Residual |  1881.11399     2,087   .90134834   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =   -0.0001
       Total |  1881.73831     2,088  .901215667   Root MSE        =    .94939

---------------------------------------------------------------------------------
      std_edu_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
std_edu_1_treat |   .0345755    .041544     0.83   0.405    -.0468966    .1160476
          _cons |    .108336   .0294163     3.68   0.000     .0506477    .1660243
---------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.02
       Model |  .001516012         1  .001516012   Prob > F        =    0.8863
    Residual |  154.768221     2,087  .074158228   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  154.769737     2,088  .074123437   Root MSE        =    .27232

------------------------------------------------------------------------------------
      unemployed_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
unemployed_1_treat |  -.0017038   .0119163    -0.14   0.886    -.0250729    .0216653
             _cons |   .0814355   .0084376     9.65   0.000     .0648885    .0979826
------------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.00
       Model |  .000257439         1  .000257439   Prob > F        =    0.9689
    Residual |  354.287516     2,087  .169759231   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  354.287773     2,088  .169678052   Root MSE        =    .41202

------------------------------------------------------------------------------
       m5s_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 m5s_1_treat |  -.0007021   .0180293    -0.04   0.969    -.0360594    .0346552
       _cons |   .2167975   .0127661    16.98   0.000     .1917619    .2418331
------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.00
       Model |  .000434163         1  .000434163   Prob > F        =    0.9630
    Residual |  421.572917     2,087  .201999481   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  421.573352     2,088  .201902946   Root MSE        =    .44944

------------------------------------------------------------------------------
        pd_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  pd_1_treat |   .0009118    .019667     0.05   0.963    -.0376572    .0394807
       _cons |   .2800122   .0139257    20.11   0.000     .2527025    .3073219
------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.00
       Model |  .000031658         1  .000031658   Prob > F        =    0.9859
    Residual |  212.014646     2,087  .101588235   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  212.014677     2,088  .101539597   Root MSE        =    .31873

-------------------------------------------------------------------------------
      lr_no_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
lr_no_1_treat |   .0002462   .0139471     0.02   0.986    -.0271055    .0275979
        _cons |   .1145079   .0098756    11.60   0.000     .0951409    .1338749
-------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.00
       Model |  .003502858         1  .003502858   Prob > F        =    0.9459
    Residual |     1588.33     2,087  .761058936   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |   1588.3335     2,088  .760696122   Root MSE        =    .87239

-----------------------------------------------------------------------------------------
      std_polinterest_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
std_polinterest_1_treat |   .0025898   .0381743     0.07   0.946    -.0722739    .0774536
                  _cons |   .2108419   .0270303     7.80   0.000     .1578328     .263851
-----------------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.00
       Model |  .000324099         1  .000324099   Prob > F        =    0.9844
    Residual |  1767.82312     2,087  .847064265   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  1767.82345     2,088  .846658738   Root MSE        =    .92036

-------------------------------------------------------------------------------------------
      std_talk_politics_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
std_talk_politics_1_treat |   .0007878   .0402736     0.02   0.984    -.0781929    .0797684
                    _cons |   .1379889   .0285167     4.84   0.000     .0820647    .1939131
-------------------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.00
       Model |  2.2223e-06         1  2.2223e-06   Prob > F        =    0.9988
    Residual |  2080.57284     2,087  .996920384   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  2080.57284     2,088  .996442933   Root MSE        =    .99846

-----------------------------------------------------------------------------------------
      std_pol_complex_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
std_pol_complex_1_treat |  -.0000652   .0436911    -0.00   0.999    -.0857478    .0856174
                  _cons |   .0886834   .0309365     2.87   0.004     .0280138    .1493531
-----------------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.00
       Model |   .00420355         1   .00420355   Prob > F        =    0.9486
    Residual |  2108.94888     2,087  1.01051695   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  2108.95308     2,088    1.010035   Root MSE        =    1.0052

-----------------------------------------------------------------------------------------------
      std_internal_efficacy_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------------+----------------------------------------------------------------
std_internal_efficacy_1_treat |  -.0028371    .043988    -0.06   0.949     -.089102    .0834279
                        _cons |    .080424   .0311468     2.58   0.010      .019342     .141506
-----------------------------------------------------------------------------------------------
(sum of wgt is 2,676.66799307593)

      Source |       SS           df       MS      Number of obs   =     2,089
-------------+----------------------------------   F(1, 2087)      =      0.00
       Model |  .001981526         1  .001981526   Prob > F        =    0.9652
    Residual |  2168.05797     2,087  1.03883947   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  2168.05995     2,088  1.03834289   Root MSE        =    1.0192

-----------------------------------------------------------------------------------------
      std_eco_general_1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
std_eco_general_1_treat |  -.0019479   .0446002    -0.04   0.965    -.0894134    .0855176
                  _cons |   .0668157   .0315803     2.12   0.034     .0048837    .1287478
-----------------------------------------------------------------------------------------

.         ** coefplot: 
.         coefplot ///
>         (raw_*) ///
>         (wgt_*, msymbol(D)) ///
>         , keep(*treat) xline(0) ///
>         ciopts(lwidth(0.4 ..)) mlwidth(medthick) msize(medsmall) mfcolor(white) ///
>         legend(order(2 "raw" 4 "entropy balanced")) ///
>         xtitle("standardized {it:differences} in means") ///
>         groups(female_1_treat unemployed_1_treat = "{bf:socio-economic}" m5s_1_treat std_internal_efficacy_1_treat = "{bf:politics
>  & voting}", angle(rvertical)) ///
>         grid(none) ///
>         ylabel( ///
>         1 "female {it:(0,1)}" ///
>         2 "age {it:(19-85)}" ///
>         3 "education {it:(1-4)}" ///
>         4 "unemployed {it:(0,1)}" ///
>         6 "M5S voter 2013 {it:(0,1)}" ///
>         7 "PD voter 2013 {it:(0,1)}" ///
>         8 "explicitly no lr placement {it:(0,1)}" ///
>         9 "political interest {it:(1-4)}" ///
>         10 "talk politics {it:(1-6)}" ///
>         11 "politics too complex {it:(1-4)}" ///
>         12 "internal efficacy {it:(1-4)}" ///
>         14 "economy retrospective {it:(1-5)}" ///
>         ) 

.         *** save as pdf: 
.         graph export "$figures/figa9_balance.pdf", replace
file /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/results/figures/figa9_balance.pdf saved as PDF format

.         
.         // events only (tab a7): 
.         ** estimate all models: 
.         eststo clear

.         eststo: reghdfe referendum_no_wunsure m5s_n_wave_157, absorb(comune_id post) cluster(id comune_id)
(dropped 28 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      5,254
Absorbing 2 HDFE groups                           F(   1,   1015) =       8.97
Statistics robust to heteroskedasticity           Prob > F        =     0.0028
                                                  R-squared       =     0.3320
                                                  Adj R-squared   =     0.1714
Number of clusters (id)      =      2,671         Within R-sq.    =     0.0011
Number of clusters (comune_id) =      1,016       Root MSE        =     0.4534

                         (Std. err. adjusted for 1,016 clusters in id comune_id)
--------------------------------------------------------------------------------
               |               Robust
referendum_n~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
m5s_n_wave_157 |   .1202458   .0401457     3.00   0.003     .0414676    .1990239
         _cons |   .4188205   .0123605    33.88   0.000     .3945654    .4430756
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |      1016        1016           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

.         eststo: reghdfe referendum_no_wunsure m5s_n_wave_157 `control_soc', absorb(comune_id post) cluster(id comune_id)
(dropped 36 singleton observations)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      5,206
Absorbing 2 HDFE groups                           F(   7,   1005) =      55.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3922
                                                  Adj R-squared   =     0.2451
Number of clusters (id)      =      2,656         Within R-sq.    =     0.0898
Number of clusters (comune_id) =      1,006       Root MSE        =     0.4330

                         (Std. err. adjusted for 1,006 clusters in id comune_id)
--------------------------------------------------------------------------------
               |               Robust
referendum_n~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
m5s_n_wave_157 |    .141771   .0419979     3.38   0.001     .0593573    .2241847
   eco_general |  -.1614167   .0096897   -16.66   0.000    -.1804311   -.1424023
  unemployed_3 |  -.0463326    .020061    -2.31   0.021    -.0856989   -.0069663
      female_3 |  -.0612072   .0201668    -3.04   0.002    -.1007811   -.0216333
         age_3 |  -.0003188   .0006576    -0.48   0.628    -.0016093    .0009716
         edu_3 |  -.0017195   .0090824    -0.19   0.850    -.0195422    .0161032
   religious_3 |  -.0703509   .0220994    -3.18   0.002     -.113717   -.0269847
         _cons |   .9688858   .0647098    14.97   0.000     .8419039    1.095868
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |      1006        1006           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

.         eststo: reghdfe referendum_no_wunsure m5s_n_wave_157 `control_soc' `control_pol', absorb(comune_id post) cluster(id comune
> _id)
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(  15,    855) =      43.66
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4533
                                                  Adj R-squared   =     0.3042
Number of clusters (id)      =      2,064         Within R-sq.    =     0.1444
Number of clusters (comune_id) =        856       Root MSE        =     0.4166

                                (Std. err. adjusted for 856 clusters in id comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
referendum_no_wun~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
     m5s_n_wave_157 |   .1251559     .04551     2.75   0.006     .0358315    .2144804
        eco_general |  -.1660494   .0131242   -12.65   0.000    -.1918089   -.1402899
       unemployed_3 |  -.0272467   .0240499    -1.13   0.258    -.0744505    .0199572
           female_3 |  -.0176435   .0216777    -0.81   0.416    -.0601913    .0249042
              age_3 |  -.0011916   .0007807    -1.53   0.127     -.002724    .0003407
              edu_3 |  -.0133522    .009987    -1.34   0.182    -.0329541    .0062496
        religious_3 |  -.0404155   .0269427    -1.50   0.134    -.0932972    .0124662
              m5s_1 |   .0725416    .032781     2.21   0.027     .0082009    .1368823
               pd_1 |  -.1258694   .0273654    -4.60   0.000    -.1795807   -.0721581
      eco_general_1 |  -.0137259   .0135605    -1.01   0.312    -.0403417    .0128899
            lr_no_1 |   .0069815   .0363885     0.19   0.848    -.0644398    .0784027
      polinterest_1 |   .0428879   .0204021     2.10   0.036     .0028439    .0829319
    talk_politics_1 |   .0237457   .0092163     2.58   0.010     .0056564     .041835
      pol_complex_1 |   .0032426      .0119     0.27   0.785     -.020114    .0265992
internal_efficacy_1 |   .0134464   .0111339     1.21   0.227    -.0084067    .0352994
              _cons |   .8176973   .1010624     8.09   0.000     .6193377    1.016057
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

.         eststo: reghdfe referendum_no_wunsure m5s_n_wave_157, absorb(id post) cluster(id comune_id)
(dropped 116 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      5,166
Absorbing 2 HDFE groups                           F(   1,   1011) =       8.38
Statistics robust to heteroskedasticity           Prob > F        =     0.0039
                                                  R-squared       =     0.7549
                                                  Adj R-squared   =     0.5092
Number of clusters (id)      =      2,583         Within R-sq.    =     0.0027
Number of clusters (comune_id) =      1,012       Root MSE        =     0.3489

                         (Std. err. adjusted for 1,012 clusters in id comune_id)
--------------------------------------------------------------------------------
               |               Robust
referendum_n~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
m5s_n_wave_157 |   .1127693    .038961     2.89   0.004     .0363157     .189223
         _cons |   .4207769   .0119221    35.29   0.000     .3973819    .4441719
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          id |      2583        2583           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)

.         ebalance m5s_ever `control_soc' `control_pol'


Data Setup
Treatment variable:   m5s_ever
Covariate adjustment: eco_general unemployed_3 female_3 age_3 edu_3 religious_3 m5s_1 pd_1 eco_general_1 lr_no_1 polinterest_1 talk_
> politics_1 pol_complex_1 internal_efficacy_1 

Optimizing...
Iteration 1: Max Difference = 11441.4483
Iteration 2: Max Difference = 4207.68638
Iteration 3: Max Difference = 1546.53494
Iteration 4: Max Difference = 567.554361
Iteration 5: Max Difference = 207.41382
Iteration 6: Max Difference = 74.9425038
Iteration 7: Max Difference = 26.2543719
Iteration 8: Max Difference = 8.46000096
Iteration 9: Max Difference = 2.185187
Iteration 10: Max Difference = .332235182
Iteration 11: Max Difference = .01710886
Iteration 12: Max Difference = .000080297
maximum difference smaller than the tolerance level; convergence achieved


Treated units: 4018    total of weights: 4018
Control units: 2233    total of weights: 4018


Before: without weighting

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |     2.351      .8945      .1253 
unemployed_3 |     .4567      .2482      .1739 |     .4971      .2501     .01164 
    female_3 |     .4216      .2439      .3175 |     .4339      .2457      .2666 
       age_3 |     52.52      234.2    -.03322 |     51.26      231.4     .05314 
       edu_3 |     5.183      1.754     -1.227 |     4.815      2.236     -.9771 
 religious_3 |     .7857      .1684     -1.393 |     .8186      .1485     -1.654 
       m5s_1 |     .2165      .1697      1.377 |     .2262      .1751      1.309 
        pd_1 |     .2805      .2019      .9773 |     .2642      .1945       1.07 
eco_genera~1 |     1.895      .7781      .8958 |      1.83      .6965      .9648 
     lr_no_1 |     .1147      .1016      2.418 |     .1303      .1134      2.196 
polinteres~1 |     3.179      .4515     -.3309 |     3.109      .4841     -.4292 
talk_polit~1 |     4.716      2.031      -.768 |     4.679      2.099      -.703 
pol_comple~1 |     2.434      .9562       .118 |     2.338      .9988      .2854 
internal_e~1 |     2.153       .988      .4234 |      2.18      .9894      .3298 


After:  _webal as the weighting variable

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |      2.41      .8877      .0831 
unemployed_3 |     .4567      .2482      .1739 |     .4567      .2482      .1739 
    female_3 |     .4216      .2439      .3175 |     .4216       .244      .3175 
       age_3 |     52.52      234.2    -.03322 |     52.52      228.6     -.0179 
       edu_3 |     5.183      1.754     -1.227 |     5.183      1.652     -1.291 
 religious_3 |     .7857      .1684     -1.393 |     .7857      .1684     -1.393 
       m5s_1 |     .2165      .1697      1.377 |     .2165      .1697      1.377 
        pd_1 |     .2805      .2019      .9773 |     .2805      .2019      .9773 
eco_genera~1 |     1.895      .7781      .8958 |     1.895      .7354      .9299 
     lr_no_1 |     .1147      .1016      2.418 |     .1147      .1016      2.418 
polinteres~1 |     3.179      .4515     -.3309 |     3.179      .4567     -.4357 
talk_polit~1 |     4.716      2.031      -.768 |     4.716      2.013      -.709 
pol_comple~1 |     2.434      .9562       .118 |     2.434       1.02      .1746 
internal_e~1 |     2.153       .988      .4234 |     2.153      .9738      .3348 

.         eststo: reghdfe referendum_no_wunsure m5s_n_wave_157 [aweight=_webal], absorb(comune_id post) cluster(id comune_id)
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(   1,    855) =       6.85
Statistics robust to heteroskedasticity           Prob > F        =     0.0090
                                                  R-squared       =     0.4175
                                                  Adj R-squared   =     0.2619
Number of clusters (id)      =      2,064         Within R-sq.    =     0.0009
Number of clusters (comune_id) =        856       Root MSE        =     0.4288

                           (Std. err. adjusted for 856 clusters in id comune_id)
--------------------------------------------------------------------------------
               |               Robust
referendum_n~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
m5s_n_wave_157 |   .1160906    .044368     2.62   0.009     .0290076    .2031736
         _cons |   .4415273   .0108962    40.52   0.000     .4201408    .4629138
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)

.         ** create a LaTeX Table:
.         estfe est*, labels(id "Individual FE" post "Wave FE" comune_id "Municipality FE")

.         ** save table:
.         esttab est* using "$tables/taba7_events.tex", replace  ///
>         indicate( `r(indicate_fe)' "Socio-economic controls=unemployed_3" "Political controls=pd_1", labels(\checkmark)) ///
>         se nobaselevels nostar label se(2) b(2) ///
>         drop(eco_general female_3 age_3 edu_3 religious_3 polinterest_1 talk_politics_1 eco_general_1 lr_no_1 pol_complex_1 intern
> al_efficacy_1) ///
>         stats(N N_clust2 r2_a r2_a_within rmse, labels("Obs" "Municipalities" "adj.R\$^2$" "adj.R\$^2$ (within)" "RMSE")) ///
>         note("\emph{Note:} Clustered standard errors by individual$\times \$ municipality in parentheses. Controls omitted from ta
> ble: economy retrospective (1-5), unemployed (0,1), female (0,1), age (18-88), education (1-7), religiosity (0,1), PD voter in 201
> 3 (0,1), political interest (1-4), talk politics (1-6), explicitly no left-right self-placement (0,1), politics too complex (1-4),
>  internal efficacy (1-4). For entropy balancing we use only variables asked in the 2013 post election study as outlined in Figure 
> \ref{fig:iv_balance}.") ///
>         substitute(_ _) 
(output written to /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/results/tables/taba7_events.tex)

.         
.         // heterogenoues effects (tab a10): 
.         ** estimate:
.         eststo clear

.         foreach dv of varlist referendum_no_wunsure turnout { 
  2.                 eststo: reghdfe `dv' c.m5s_wn_wave_157##i.m5s_1 `control_soc' pd_1 pdl_1 lr_no_1 polinterest_1 talk_politics_1 
> eco_general_1 pol_complex_1 internal_efficacy_1, absorb(comune_id post) cluster(id comune_id)
  3.                 eststo: reghdfe `dv' c.m5s_wn_wave_157##i.pd_1 m5s_1 pdl_1 `control_soc' lr_no_1 polinterest_1 talk_politics_1 
> eco_general_1 pol_complex_1 internal_efficacy_1, absorb(comune_id post) cluster(id comune_id)
  4.                 eststo: reghdfe `dv' c.m5s_wn_wave_157##i.pdl_1 pd_1 m5s_1 `control_soc' lr_no_1 polinterest_1 talk_politics_1 
> eco_general_1 pol_complex_1 internal_efficacy_1, absorb(comune_id post) cluster(id comune_id)
  5.                 eststo: reghdfe `dv' c.m5s_wn_wave_157##i.referendum_unsure_3 pdl_1 pd_1 m5s_1 `control_soc' lr_no_1 polinteres
> t_1 talk_politics_1 eco_general_1 pol_complex_1 internal_efficacy_1, absorb(comune_id post) cluster(id comune_id)
  6.         }
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(  17,    855) =      44.92
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4582
                                                  Adj R-squared   =     0.3100
Number of clusters (id)      =      2,064         Within R-sq.    =     0.1521
Number of clusters (comune_id) =        856       Root MSE        =     0.4149

                                    (Std. err. adjusted for 856 clusters in id comune_id)
-----------------------------------------------------------------------------------------
                        |               Robust
  referendum_no_wunsure | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
        m5s_wn_wave_157 |   .0637802   .0275121     2.32   0.021     .0097811    .1177794
                1.m5s_1 |   .0626804   .0392123     1.60   0.110    -.0142834    .1396441
                        |
m5s_1#c.m5s_wn_wave_157 |
                     1  |   .0429711   .0222967     1.93   0.054    -.0007915    .0867337
                        |
            eco_general |  -.1656142   .0127749   -12.96   0.000     -.190688   -.1405405
           unemployed_3 |  -.0313732   .0236138    -1.33   0.184    -.0777209    .0149746
               female_3 |  -.0130891   .0216023    -0.61   0.545    -.0554889    .0293107
                  age_3 |  -.0013234   .0007516    -1.76   0.079    -.0027986    .0001518
                  edu_3 |  -.0133026   .0099103    -1.34   0.180     -.032754    .0061489
            religious_3 |  -.0533197   .0279444    -1.91   0.057    -.1081673    .0015279
                   pd_1 |  -.0852797   .0285624    -2.99   0.003    -.1413403   -.0292191
                  pdl_1 |   .1359632   .0370733     3.67   0.000     .0631977    .2087286
                lr_no_1 |   .0164254   .0365264     0.45   0.653    -.0552664    .0881173
          polinterest_1 |   .0404911   .0196434     2.06   0.040     .0019362    .0790461
        talk_politics_1 |   .0245914   .0089721     2.74   0.006     .0069815    .0422014
          eco_general_1 |  -.0087411   .0134252    -0.65   0.515    -.0350913    .0176091
          pol_complex_1 |   .0041697   .0117279     0.36   0.722    -.0188491    .0271885
    internal_efficacy_1 |   .0124975   .0111132     1.12   0.261    -.0093148    .0343098
                  _cons |   .7718135    .104575     7.38   0.000     .5665597    .9770672
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(  17,    855) =      44.26
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4576
                                                  Adj R-squared   =     0.3093
Number of clusters (id)      =      2,064         Within R-sq.    =     0.1512
Number of clusters (comune_id) =        856       Root MSE        =     0.4151

                                   (Std. err. adjusted for 856 clusters in id comune_id)
----------------------------------------------------------------------------------------
                       |               Robust
 referendum_no_wunsure | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
       m5s_wn_wave_157 |   .0767834    .029889     2.57   0.010      .018119    .1354478
                1.pd_1 |  -.0697857   .0361872    -1.93   0.054    -.1408119    .0012404
                       |
pd_1#c.m5s_wn_wave_157 |
                    1  |   -.013477   .0251861    -0.54   0.593    -.0629108    .0359567
                       |
                 m5s_1 |   .1113188   .0345275     3.22   0.001     .0435502    .1790875
                 pdl_1 |   .1360333   .0371832     3.66   0.000     .0630522    .2090144
           eco_general |  -.1648553   .0127373   -12.94   0.000    -.1898554   -.1398552
          unemployed_3 |  -.0296952   .0235571    -1.26   0.208    -.0759316    .0165413
              female_3 |  -.0127616    .021732    -0.59   0.557    -.0554158    .0298927
                 age_3 |  -.0013543   .0007441    -1.82   0.069    -.0028148    .0001061
                 edu_3 |    -.01256   .0099181    -1.27   0.206    -.0320266    .0069066
           religious_3 |  -.0548578   .0282343    -1.94   0.052    -.1102745     .000559
               lr_no_1 |   .0172552   .0363336     0.47   0.635    -.0540584    .0885687
         polinterest_1 |   .0402725   .0196916     2.05   0.041      .001623     .078922
       talk_politics_1 |   .0245374   .0089418     2.74   0.006      .006987    .0420879
         eco_general_1 |  -.0086532   .0133209    -0.65   0.516    -.0347986    .0174923
         pol_complex_1 |   .0043065   .0117869     0.37   0.715    -.0188281    .0274411
   internal_efficacy_1 |   .0133971   .0111926     1.20   0.232    -.0085712    .0353653
                 _cons |   .7514408    .103996     7.23   0.000     .5473235    .9555581
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(  17,    855) =      43.64
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4576
                                                  Adj R-squared   =     0.3092
Number of clusters (id)      =      2,064         Within R-sq.    =     0.1512
Number of clusters (comune_id) =        856       Root MSE        =     0.4151

                                    (Std. err. adjusted for 856 clusters in id comune_id)
-----------------------------------------------------------------------------------------
                        |               Robust
  referendum_no_wunsure | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
        m5s_wn_wave_157 |    .074425   .0271782     2.74   0.006     .0210812    .1277688
                1.pdl_1 |   .1492268   .0577539     2.58   0.010     .0358707    .2625829
                        |
pdl_1#c.m5s_wn_wave_157 |
                     1  |  -.0105294   .0339165    -0.31   0.756    -.0770986    .0560399
                        |
                   pd_1 |  -.0845081   .0287562    -2.94   0.003    -.1409491   -.0280671
                  m5s_1 |    .111142   .0345741     3.21   0.001      .043282    .1790019
            eco_general |  -.1648468   .0127447   -12.93   0.000    -.1898614   -.1398322
           unemployed_3 |  -.0300429   .0236059    -1.27   0.203    -.0763752    .0162895
               female_3 |  -.0129137   .0217322    -0.59   0.553    -.0555684    .0297409
                  age_3 |  -.0013721   .0007417    -1.85   0.065    -.0028279    .0000836
                  edu_3 |  -.0127369   .0099413    -1.28   0.200    -.0322491    .0067752
            religious_3 |  -.0542587   .0284643    -1.91   0.057    -.1101269    .0016094
                lr_no_1 |   .0175896   .0365844     0.48   0.631    -.0542162    .0893954
          polinterest_1 |   .0400347   .0199552     2.01   0.045     .0008678    .0792016
        talk_politics_1 |   .0244771   .0089412     2.74   0.006     .0069278    .0420264
          eco_general_1 |  -.0082376   .0133425    -0.62   0.537    -.0344255    .0179503
          pol_complex_1 |    .004406   .0117889     0.37   0.709    -.0187324    .0275445
    internal_efficacy_1 |   .0133916   .0111731     1.20   0.231    -.0085384    .0353216
                  _cons |   .7551547   .1045976     7.22   0.000     .5498566    .9604527
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)
(dropped 6 singleton observations)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      4,018
Absorbing 2 HDFE groups                           F(  18,    854) =      55.29
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4838
                                                  Adj R-squared   =     0.3403
Number of clusters (id)      =      2,012         Within R-sq.    =     0.1864
Number of clusters (comune_id) =        855       Root MSE        =     0.4056

                                                  (Std. err. adjusted for 855 clusters in id comune_id)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                referendum_no_wunsure | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------------------------+----------------------------------------------------------------
                      m5s_wn_wave_157 |    .060914   .0265267     2.30   0.022     .0088488    .1129793
                1.referendum_unsure_3 |  -.2661077   .0303555    -8.77   0.000    -.3256878   -.2065277
                                      |
referendum_unsure_3#c.m5s_wn_wave_157 |
                                   1  |   .0339973   .0194872     1.74   0.081    -.0042512    .0722458
                                      |
                                pdl_1 |   .1172024   .0367498     3.19   0.001     .0450718    .1893329
                                 pd_1 |  -.0946843   .0273981    -3.46   0.001    -.1484598   -.0409089
                                m5s_1 |   .1051054   .0325031     3.23   0.001     .0413101    .1689008
                          eco_general |  -.1631181   .0122385   -13.33   0.000    -.1871392   -.1390971
                         unemployed_3 |  -.0217858   .0228436    -0.95   0.341    -.0666219    .0230503
                             female_3 |   .0014065   .0214988     0.07   0.948    -.0407901    .0436031
                                age_3 |  -.0022987   .0008105    -2.84   0.005    -.0038896   -.0007079
                                edu_3 |  -.0125607   .0099838    -1.26   0.209    -.0321564     .007035
                          religious_3 |  -.0516038   .0259979    -1.98   0.047     -.102631   -.0005766
                              lr_no_1 |   .0064029   .0327878     0.20   0.845    -.0579511    .0707569
                        polinterest_1 |   .0169215   .0184602     0.92   0.360    -.0193112    .0531541
                      talk_politics_1 |   .0211658    .010272     2.06   0.040     .0010045     .041327
                        eco_general_1 |  -.0105749   .0140147    -0.75   0.451    -.0380821    .0169323
                        pol_complex_1 |   .0035298   .0123092     0.29   0.774    -.0206301    .0276896
                  internal_efficacy_1 |   .0106085   .0116627     0.91   0.363    -.0122824    .0334994
                                _cons |   .9744578   .1116841     8.73   0.000     .7552504    1.193665
-------------------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       855         855           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)
(dropped 6 singleton observations)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      4,156
Absorbing 2 HDFE groups                           F(  17,    872) =       4.55
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3169
                                                  Adj R-squared   =     0.1304
Number of clusters (id)      =      2,081         Within R-sq.    =     0.0230
Number of clusters (comune_id) =        873       Root MSE        =     0.2887

                                    (Std. err. adjusted for 873 clusters in id comune_id)
-----------------------------------------------------------------------------------------
                        |               Robust
                turnout | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
        m5s_wn_wave_157 |   .0405513   .0225428     1.80   0.072    -.0036932    .0847958
                1.m5s_1 |   .0280681   .0286894     0.98   0.328    -.0282401    .0843764
                        |
m5s_1#c.m5s_wn_wave_157 |
                     1  |  -.0016125   .0183792    -0.09   0.930    -.0376851    .0344601
                        |
            eco_general |  -.0013448   .0067599    -0.20   0.842    -.0146124    .0119228
           unemployed_3 |  -.0077692   .0132536    -0.59   0.558    -.0337819    .0182434
               female_3 |  -.0071007   .0138322    -0.51   0.608     -.034249    .0200476
                  age_3 |   .0012267   .0005345     2.30   0.022     .0001777    .0022757
                  edu_3 |   .0121671    .005753     2.11   0.035     .0008759    .0234584
            religious_3 |   .0181385   .0184131     0.99   0.325    -.0180007    .0542778
                   pd_1 |   .0481605   .0178804     2.69   0.007     .0130668    .0832542
                  pdl_1 |  -.0107894   .0248006    -0.44   0.664    -.0594653    .0378865
                lr_no_1 |  -.0095462   .0206252    -0.46   0.644    -.0500271    .0309346
          polinterest_1 |   .0295948   .0135742     2.18   0.030     .0029528    .0562367
        talk_politics_1 |   .0137069    .007475     1.83   0.067    -.0009641    .0283779
          eco_general_1 |  -.0168746    .010584    -1.59   0.111    -.0376476    .0038984
          pol_complex_1 |   -.001842   .0062876    -0.29   0.770    -.0141826    .0104987
    internal_efficacy_1 |   .0031334   .0064932     0.48   0.630    -.0096107    .0158775
                  _cons |   .5825647   .0558837    10.42   0.000     .4728825    .6922469
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       873         873           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)
(dropped 6 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,156
Absorbing 2 HDFE groups                           F(  17,    872) =       4.48
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3169
                                                  Adj R-squared   =     0.1304
Number of clusters (id)      =      2,081         Within R-sq.    =     0.0230
Number of clusters (comune_id) =        873       Root MSE        =     0.2887

                                   (Std. err. adjusted for 873 clusters in id comune_id)
----------------------------------------------------------------------------------------
                       |               Robust
               turnout | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
       m5s_wn_wave_157 |   .0401012   .0220567     1.82   0.069    -.0031892    .0833916
                1.pd_1 |   .0476474   .0273375     1.74   0.082    -.0060077    .1013024
                       |
pd_1#c.m5s_wn_wave_157 |
                    1  |   .0004445   .0153028     0.03   0.977    -.0295902    .0304791
                       |
                 m5s_1 |   .0262445   .0223251     1.18   0.240    -.0175727    .0700617
                 pdl_1 |     -.0108   .0248092    -0.44   0.663    -.0594927    .0378928
           eco_general |  -.0013724   .0067146    -0.20   0.838    -.0145511    .0118063
          unemployed_3 |  -.0078365   .0132219    -0.59   0.554    -.0337869    .0181138
              female_3 |  -.0071125   .0139049    -0.51   0.609    -.0344035    .0201786
                 age_3 |   .0012278    .000531     2.31   0.021     .0001855    .0022701
                 edu_3 |   .0121373   .0057447     2.11   0.035     .0008622    .0234124
           religious_3 |   .0181949   .0181393     1.00   0.316    -.0174069    .0537967
               lr_no_1 |  -.0095765   .0206573    -0.46   0.643    -.0501203    .0309673
         polinterest_1 |   .0296043   .0135276     2.19   0.029     .0030539    .0561547
       talk_politics_1 |   .0137086   .0074693     1.84   0.067    -.0009514    .0283686
         eco_general_1 |  -.0168775   .0105783    -1.60   0.111    -.0376394    .0038845
         pol_complex_1 |  -.0018483   .0062678    -0.29   0.768    -.0141501    .0104535
   internal_efficacy_1 |   .0030995    .006536     0.47   0.635    -.0097286    .0159276
                 _cons |   .5833062   .0567417    10.28   0.000       .47194    .6946725
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       873         873           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est6 stored)
(dropped 6 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,156
Absorbing 2 HDFE groups                           F(  17,    872) =       4.52
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3170
                                                  Adj R-squared   =     0.1305
Number of clusters (id)      =      2,081         Within R-sq.    =     0.0232
Number of clusters (comune_id) =        873       Root MSE        =     0.2886

                                    (Std. err. adjusted for 873 clusters in id comune_id)
-----------------------------------------------------------------------------------------
                        |               Robust
                turnout | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
        m5s_wn_wave_157 |   .0385249    .020882     1.84   0.065    -.0024601    .0795098
                1.pdl_1 |  -.0251215   .0345728    -0.73   0.468     -.092977    .0427341
                        |
pdl_1#c.m5s_wn_wave_157 |
                     1  |   .0118031   .0198256     0.60   0.552    -.0271085    .0507146
                        |
                   pd_1 |    .047851    .017996     2.66   0.008     .0125305    .0831715
                  m5s_1 |   .0260772   .0224624     1.16   0.246    -.0180095    .0701639
            eco_general |  -.0013584   .0066803    -0.20   0.839    -.0144697    .0117529
           unemployed_3 |  -.0074983   .0133338    -0.56   0.574    -.0336684    .0186719
               female_3 |  -.0070367   .0138072    -0.51   0.610     -.034136    .0200626
                  age_3 |   .0012341   .0005283     2.34   0.020     .0001972    .0022709
                  edu_3 |   .0121857   .0057313     2.13   0.034      .000937    .0234344
            religious_3 |   .0175868   .0182371     0.96   0.335    -.0182069    .0533805
                lr_no_1 |  -.0091988   .0207509    -0.44   0.658    -.0499263    .0315286
          polinterest_1 |   .0300312   .0137805     2.18   0.030     .0029845     .057078
        talk_politics_1 |   .0136925    .007457     1.84   0.067    -.0009433    .0283282
          eco_general_1 |  -.0169377   .0105388    -1.61   0.108     -.037622    .0037466
          pol_complex_1 |  -.0019463   .0062555    -0.31   0.756    -.0142238    .0103312
    internal_efficacy_1 |   .0031861    .006497     0.49   0.624    -.0095655    .0159376
                  _cons |   .5840066   .0565144    10.33   0.000     .4730864    .6949268
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       873         873           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)
(dropped 6 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,018
Absorbing 2 HDFE groups                           F(  18,    854) =      16.77
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3765
                                                  Adj R-squared   =     0.2031
Number of clusters (id)      =      2,012         Within R-sq.    =     0.1143
Number of clusters (comune_id) =        855       Root MSE        =     0.2563

                                                  (Std. err. adjusted for 855 clusters in id comune_id)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                              turnout | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------------------------+----------------------------------------------------------------
                      m5s_wn_wave_157 |   .0327308   .0152221     2.15   0.032     .0028536     .062608
                1.referendum_unsure_3 |  -.2203401   .0244953    -9.00   0.000    -.2684181   -.1722621
                                      |
referendum_unsure_3#c.m5s_wn_wave_157 |
                                   1  |    .001169   .0172809     0.07   0.946    -.0327491    .0350871
                                      |
                                pdl_1 |  -.0042073   .0171351    -0.25   0.806    -.0378391    .0294244
                                 pd_1 |   .0379211   .0174679     2.17   0.030     .0036361    .0722061
                                m5s_1 |   .0208193   .0199904     1.04   0.298    -.0184168    .0600553
                          eco_general |  -.0011641   .0061643    -0.19   0.850    -.0132632    .0109349
                         unemployed_3 |   .0049836   .0121325     0.41   0.681    -.0188295    .0287966
                             female_3 |   .0030209   .0118137     0.26   0.798    -.0201664    .0262082
                                age_3 |    .000596   .0003661     1.63   0.104    -.0001224    .0013145
                                edu_3 |   .0124087   .0045769     2.71   0.007     .0034253    .0213921
                          religious_3 |   .0305539   .0135488     2.26   0.024     .0039611    .0571468
                              lr_no_1 |   .0052619   .0168637     0.31   0.755    -.0278372    .0383611
                        polinterest_1 |   .0144863   .0105669     1.37   0.171    -.0062539    .0352264
                      talk_politics_1 |   .0050517   .0046083     1.10   0.273    -.0039932    .0140966
                        eco_general_1 |  -.0153556   .0081158    -1.89   0.059    -.0312848    .0005736
                        pol_complex_1 |   -.001489   .0045255    -0.33   0.742    -.0103713    .0073933
                  internal_efficacy_1 |  -.0003614   .0054265    -0.07   0.947    -.0110122    .0102894
                                _cons |   .7738243   .0491337    15.75   0.000     .6773873    .8702613
-------------------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       855         855           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est8 stored)

.         ** create a LaTeX Table:
.         estfe est*, labels(post "Wave FE" comune_id "Municipality FE")

.         ** save table: 
.         esttab est* using "$tables/taba10_hte.tex", replace  ///
>         indicate( `r(indicate_fe)' "Socio-economic controls=unemployed_3" "Political controls=pd_1", labels(\checkmark)) ///
>         se nobaselevels nostar label se(2) b(2) ///
>         drop(eco_general female_3 age_3 edu_3 religious_3  pdl_1  lr_no_1 polinterest_1 talk_politics_1 eco_general_1  pol_complex
> _1 internal_efficacy_1) ///
>         stats(N N_clust2 r2_a r2_a_within rmse,  fmt(%9.0f %9.0f %9.2f %9.2f %9.2f) labels("Obs" "Municipalities" "adj.R\$^2$" "ad
> j.R\$^2$ (within)" "RMSE")) ///
>         note("\emph{Note:} Clustered standard errors by individual$\times \$ municipality in parentheses. Controls omitted from ta
> ble: economy retrospective (1-5), unemployed (0,1), female (0,1), age (18-88), education (1-7), religiosity (0,1), PD voter in 201
> 3 (0,1), political interest (1-4), talk politics (1-6), explicitly no left-right self-placement (0,1), politics too complex (1-4),
>  internal efficacy (1-4). For entropy balancing we use only variables asked in the 2013 post election study as outlined in Figure 
> \ref{fig:iv_balance}.") ///
>         substitute(_ _) ///
>         mgroups("Vote: No" "Turnout", pattern( 0 0 0 0 1 0 0 0 1) ///
>         prefix(\multicolumn{@span}{c}{) suffix(}) ///
>         span erepeat(\cmidrule(lr){@span})) 
(output written to /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/results/tables/taba10_hte.tex)

. 
.         // remaining ref outcomes (fig a11)
.         ** estimate all models: 
.         cls

.         eststo clear

.         foreach var of varlist referendum_yes_wunsure turnout {
  2.                 eststo: reghdfe `var' m5s_wn_wave_157, absorb(comune_id post) cluster(id comune_id)
  3.                 eststo: reghdfe `var' m5s_wn_wave_157 `control_soc', absorb(comune_id post) cluster(id comune_id)
  4.                 eststo: reghdfe `var' m5s_wn_wave_157 `control_soc' `control_pol', absorb(comune_id post) cluster(id comune_id)
  5.                 eststo: reghdfe `var' m5s_wn_wave_157, absorb(id post) cluster(id comune_id)
  6.                 ebalance m5s_ever `control_soc' `control_pol'
  7.                 eststo: reghdfe `var' m5s_wn_wave_157 [aweight=_webal], absorb(comune_id post) cluster(id comune_id)
  8.         }
(dropped 28 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      5,254
Absorbing 2 HDFE groups                           F(   1,   1015) =       3.91
Statistics robust to heteroskedasticity           Prob > F        =     0.0483
                                                  R-squared       =     0.3270
                                                  Adj R-squared   =     0.1653
Number of clusters (id)      =      2,671         Within R-sq.    =     0.0006
Number of clusters (comune_id) =      1,016       Root MSE        =     0.4309

                          (Std. err. adjusted for 1,016 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_ye~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |  -.0449466   .0227363    -1.98   0.048    -.0895621    -.000331
          _cons |   .3725671   .0195897    19.02   0.000     .3341261    .4110081
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |      1016        1016           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)
(dropped 36 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      5,206
Absorbing 2 HDFE groups                           F(   7,   1005) =      51.28
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4071
                                                  Adj R-squared   =     0.2636
Number of clusters (id)      =      2,656         Within R-sq.    =     0.1181
Number of clusters (comune_id) =      1,006       Root MSE        =     0.4052

                          (Std. err. adjusted for 1,006 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_ye~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |  -.0508668   .0232797    -2.19   0.029    -.0965491   -.0051844
    eco_general |   .1654047   .0097486    16.97   0.000     .1462748    .1845346
   unemployed_3 |   .0024355   .0167194     0.15   0.884    -.0303735    .0352445
       female_3 |  -.0219192   .0183882    -1.19   0.234    -.0580029    .0141645
          age_3 |   .0042674   .0007222     5.91   0.000     .0028503    .0056845
          edu_3 |   .0201689   .0074413     2.71   0.007     .0055666    .0347713
    religious_3 |   .0448898   .0226516     1.98   0.048     .0004399    .0893397
          _cons |  -.4002397   .0668592    -5.99   0.000    -.5314393   -.2690401
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |      1006        1006           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(  15,    855) =      33.89
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4657
                                                  Adj R-squared   =     0.3199
Number of clusters (id)      =      2,064         Within R-sq.    =     0.1629
Number of clusters (comune_id) =        856       Root MSE        =     0.3954

                                (Std. err. adjusted for 856 clusters in id comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
referendum_yes_wu~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    m5s_wn_wave_157 |  -.0270741   .0210762    -1.28   0.199    -.0684412     .014293
        eco_general |   .1612294   .0124939    12.90   0.000     .1367072    .1857517
       unemployed_3 |  -.0012284   .0216743    -0.06   0.955    -.0437694    .0413127
           female_3 |  -.0211011   .0192824    -1.09   0.274    -.0589475    .0167452
              age_3 |   .0035431   .0010003     3.54   0.000     .0015797    .0055064
              edu_3 |   .0134392   .0096066     1.40   0.162    -.0054161    .0322945
        religious_3 |   .0366559   .0243352     1.51   0.132    -.0111078    .0844196
              m5s_1 |  -.0682974     .02661    -2.57   0.010    -.1205259   -.0160688
               pd_1 |   .1597837   .0253274     6.31   0.000     .1100726    .2094947
      eco_general_1 |   .0187735   .0135736     1.38   0.167    -.0078679     .045415
            lr_no_1 |   .0165126   .0292382     0.56   0.572    -.0408745    .0738996
      polinterest_1 |   .0004447   .0177068     0.03   0.980    -.0343091    .0351986
    talk_politics_1 |  -.0078913   .0126357    -0.62   0.532    -.0326919    .0169093
      pol_complex_1 |   .0006473   .0118305     0.05   0.956    -.0225728    .0238675
internal_efficacy_1 |  -.0136511   .0117339    -1.16   0.245    -.0366818    .0093795
              _cons |  -.3202613   .0977535    -3.28   0.001    -.5121263   -.1283964
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)
(dropped 116 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      5,166
Absorbing 2 HDFE groups                           F(   1,   1011) =       3.38
Statistics robust to heteroskedasticity           Prob > F        =     0.0663
                                                  R-squared       =     0.7761
                                                  Adj R-squared   =     0.5518
Number of clusters (id)      =      2,583         Within R-sq.    =     0.0017
Number of clusters (comune_id) =      1,012       Root MSE        =     0.3165

                          (Std. err. adjusted for 1,012 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_ye~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |  -.0423838   .0230564    -1.84   0.066    -.0876277    .0028602
          _cons |   .3735452   .0197689    18.90   0.000     .3347525    .4123379
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          id |      2583        2583           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)


Data Setup
Treatment variable:   m5s_ever
Covariate adjustment: eco_general unemployed_3 female_3 age_3 edu_3 religious_3 m5s_1 pd_1 eco_general_1 lr_no_1 polinterest_1 talk_
> politics_1 pol_complex_1 internal_efficacy_1 

Optimizing...
Iteration 1: Max Difference = 11441.4483
Iteration 2: Max Difference = 4207.68638
Iteration 3: Max Difference = 1546.53494
Iteration 4: Max Difference = 567.554361
Iteration 5: Max Difference = 207.41382
Iteration 6: Max Difference = 74.9425038
Iteration 7: Max Difference = 26.2543719
Iteration 8: Max Difference = 8.46000096
Iteration 9: Max Difference = 2.185187
Iteration 10: Max Difference = .332235182
Iteration 11: Max Difference = .01710886
Iteration 12: Max Difference = .000080297
maximum difference smaller than the tolerance level; convergence achieved


Treated units: 4018    total of weights: 4018
Control units: 2233    total of weights: 4018


Before: without weighting

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |     2.351      .8945      .1253 
unemployed_3 |     .4567      .2482      .1739 |     .4971      .2501     .01164 
    female_3 |     .4216      .2439      .3175 |     .4339      .2457      .2666 
       age_3 |     52.52      234.2    -.03322 |     51.26      231.4     .05314 
       edu_3 |     5.183      1.754     -1.227 |     4.815      2.236     -.9771 
 religious_3 |     .7857      .1684     -1.393 |     .8186      .1485     -1.654 
       m5s_1 |     .2165      .1697      1.377 |     .2262      .1751      1.309 
        pd_1 |     .2805      .2019      .9773 |     .2642      .1945       1.07 
eco_genera~1 |     1.895      .7781      .8958 |      1.83      .6965      .9648 
     lr_no_1 |     .1147      .1016      2.418 |     .1303      .1134      2.196 
polinteres~1 |     3.179      .4515     -.3309 |     3.109      .4841     -.4292 
talk_polit~1 |     4.716      2.031      -.768 |     4.679      2.099      -.703 
pol_comple~1 |     2.434      .9562       .118 |     2.338      .9988      .2854 
internal_e~1 |     2.153       .988      .4234 |      2.18      .9894      .3298 


After:  _webal as the weighting variable

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |      2.41      .8877      .0831 
unemployed_3 |     .4567      .2482      .1739 |     .4567      .2482      .1739 
    female_3 |     .4216      .2439      .3175 |     .4216       .244      .3175 
       age_3 |     52.52      234.2    -.03322 |     52.52      228.6     -.0179 
       edu_3 |     5.183      1.754     -1.227 |     5.183      1.652     -1.291 
 religious_3 |     .7857      .1684     -1.393 |     .7857      .1684     -1.393 
       m5s_1 |     .2165      .1697      1.377 |     .2165      .1697      1.377 
        pd_1 |     .2805      .2019      .9773 |     .2805      .2019      .9773 
eco_genera~1 |     1.895      .7781      .8958 |     1.895      .7354      .9299 
     lr_no_1 |     .1147      .1016      2.418 |     .1147      .1016      2.418 
polinteres~1 |     3.179      .4515     -.3309 |     3.179      .4567     -.4357 
talk_polit~1 |     4.716      2.031      -.768 |     4.716      2.013      -.709 
pol_comple~1 |     2.434      .9562       .118 |     2.434       1.02      .1746 
internal_e~1 |     2.153       .988      .4234 |     2.153      .9738      .3348 
(dropped 23 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,070
Absorbing 2 HDFE groups                           F(   1,    855) =       1.59
Statistics robust to heteroskedasticity           Prob > F        =     0.2079
                                                  R-squared       =     0.4267
                                                  Adj R-squared   =     0.2736
Number of clusters (id)      =      2,064         Within R-sq.    =     0.0002
Number of clusters (comune_id) =        856       Root MSE        =     0.4101

                            (Std. err. adjusted for 856 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
referendum_ye~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |  -.0270639   .0214727    -1.26   0.208    -.0692093    .0150815
          _cons |   .3825314     .01469    26.04   0.000     .3536988     .411364
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       856         856           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      5,398
Absorbing 2 HDFE groups                           F(   1,   1043) =       2.84
Statistics robust to heteroskedasticity           Prob > F        =     0.0921
                                                  R-squared       =     0.3051
                                                  Adj R-squared   =     0.1380
Number of clusters (id)      =      2,699         Within R-sq.    =     0.0006
Number of clusters (comune_id) =      1,044       Root MSE        =     0.3334

                          (Std. err. adjusted for 1,044 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
        turnout | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0322994   .0191557     1.69   0.092    -.0052886    .0698875
          _cons |   .8200834    .016501    49.70   0.000     .7877045    .8524623
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |      1044        1044           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est6 stored)
(dropped 11 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      5,343
Absorbing 2 HDFE groups                           F(   7,   1031) =      13.46
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3192
                                                  Adj R-squared   =     0.1546
Number of clusters (id)      =      2,683         Within R-sq.    =     0.0210
Number of clusters (comune_id) =      1,032       Root MSE        =     0.3267

                          (Std. err. adjusted for 1,032 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
        turnout | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0314182   .0192805     1.63   0.104    -.0064153    .0692516
    eco_general |   .0061874   .0067586     0.92   0.360    -.0070748    .0194497
   unemployed_3 |   -.021051   .0143755    -1.46   0.143    -.0492595    .0071575
       female_3 |  -.0467822   .0160441    -2.92   0.004    -.0782649   -.0152994
          age_3 |   .0025397   .0005051     5.03   0.000     .0015486    .0035308
          edu_3 |   .0294892   .0061047     4.83   0.000     .0175101    .0414683
    religious_3 |   .0148979   .0162344     0.92   0.359    -.0169582    .0467541
          _cons |   .5508093   .0469958    11.72   0.000     .4585909    .6430276
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |      1032        1032           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est7 stored)
(dropped 6 singleton observations)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =      4,156
Absorbing 2 HDFE groups                           F(  15,    872) =       5.03
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3168
                                                  Adj R-squared   =     0.1308
Number of clusters (id)      =      2,081         Within R-sq.    =     0.0229
Number of clusters (comune_id) =        873       Root MSE        =     0.2886

                                (Std. err. adjusted for 873 clusters in id comune_id)
-------------------------------------------------------------------------------------
                    |               Robust
            turnout | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    m5s_wn_wave_157 |   .0402242   .0217801     1.85   0.065    -.0025233    .0829717
        eco_general |  -.0012658   .0066941    -0.19   0.850    -.0144043    .0118727
       unemployed_3 |  -.0080019   .0131449    -0.61   0.543    -.0338012    .0177974
           female_3 |  -.0067218   .0140228    -0.48   0.632    -.0342441    .0208006
              age_3 |   .0012132   .0005282     2.30   0.022     .0001765    .0022498
              edu_3 |   .0121934   .0057998     2.10   0.036     .0008102    .0235766
        religious_3 |   .0170157   .0170932     1.00   0.320    -.0165329    .0505644
              m5s_1 |   .0293385   .0190556     1.54   0.124    -.0080617    .0667387
               pd_1 |   .0514459    .017027     3.02   0.003     .0180272    .0848645
      eco_general_1 |  -.0164536   .0103529    -1.59   0.112     -.036773    .0038658
            lr_no_1 |  -.0087378   .0201626    -0.43   0.665    -.0483107     .030835
      polinterest_1 |   .0294206   .0135921     2.16   0.031     .0027436    .0560976
    talk_politics_1 |   .0137608   .0074823     1.84   0.066    -.0009247    .0284463
      pol_complex_1 |  -.0017689   .0062871    -0.28   0.779    -.0141085    .0105708
internal_efficacy_1 |   .0031004   .0065442     0.47   0.636    -.0097438    .0159446
              _cons |   .5802021    .057597    10.07   0.000     .4671572     .693247
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       873         873           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est8 stored)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      5,398
Absorbing 2 HDFE groups                           F(   1,   1043) =       2.84
Statistics robust to heteroskedasticity           Prob > F        =     0.0921
                                                  R-squared       =     0.6586
                                                  Adj R-squared   =     0.3165
Number of clusters (id)      =      2,699         Within R-sq.    =     0.0011
Number of clusters (comune_id) =      1,044       Root MSE        =     0.2969

                          (Std. err. adjusted for 1,044 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
        turnout | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0322994   .0191557     1.69   0.092    -.0052886    .0698875
          _cons |   .8200834    .016501    49.70   0.000     .7877045    .8524623
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          id |      2699        2699           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est9 stored)


Data Setup
Treatment variable:   m5s_ever
Covariate adjustment: eco_general unemployed_3 female_3 age_3 edu_3 religious_3 m5s_1 pd_1 eco_general_1 lr_no_1 polinterest_1 talk_
> politics_1 pol_complex_1 internal_efficacy_1 

Optimizing...
Iteration 1: Max Difference = 11441.4483
Iteration 2: Max Difference = 4207.68638
Iteration 3: Max Difference = 1546.53494
Iteration 4: Max Difference = 567.554361
Iteration 5: Max Difference = 207.41382
Iteration 6: Max Difference = 74.9425038
Iteration 7: Max Difference = 26.2543719
Iteration 8: Max Difference = 8.46000096
Iteration 9: Max Difference = 2.185187
Iteration 10: Max Difference = .332235182
Iteration 11: Max Difference = .01710886
Iteration 12: Max Difference = .000080297
maximum difference smaller than the tolerance level; convergence achieved


Treated units: 4018    total of weights: 4018
Control units: 2233    total of weights: 4018


Before: without weighting

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |     2.351      .8945      .1253 
unemployed_3 |     .4567      .2482      .1739 |     .4971      .2501     .01164 
    female_3 |     .4216      .2439      .3175 |     .4339      .2457      .2666 
       age_3 |     52.52      234.2    -.03322 |     51.26      231.4     .05314 
       edu_3 |     5.183      1.754     -1.227 |     4.815      2.236     -.9771 
 religious_3 |     .7857      .1684     -1.393 |     .8186      .1485     -1.654 
       m5s_1 |     .2165      .1697      1.377 |     .2262      .1751      1.309 
        pd_1 |     .2805      .2019      .9773 |     .2642      .1945       1.07 
eco_genera~1 |     1.895      .7781      .8958 |      1.83      .6965      .9648 
     lr_no_1 |     .1147      .1016      2.418 |     .1303      .1134      2.196 
polinteres~1 |     3.179      .4515     -.3309 |     3.109      .4841     -.4292 
talk_polit~1 |     4.716      2.031      -.768 |     4.679      2.099      -.703 
pol_comple~1 |     2.434      .9562       .118 |     2.338      .9988      .2854 
internal_e~1 |     2.153       .988      .4234 |      2.18      .9894      .3298 


After:  _webal as the weighting variable

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
 eco_general |      2.41      .9201      .1058 |      2.41      .8877      .0831 
unemployed_3 |     .4567      .2482      .1739 |     .4567      .2482      .1739 
    female_3 |     .4216      .2439      .3175 |     .4216       .244      .3175 
       age_3 |     52.52      234.2    -.03322 |     52.52      228.6     -.0179 
       edu_3 |     5.183      1.754     -1.227 |     5.183      1.652     -1.291 
 religious_3 |     .7857      .1684     -1.393 |     .7857      .1684     -1.393 
       m5s_1 |     .2165      .1697      1.377 |     .2165      .1697      1.377 
        pd_1 |     .2805      .2019      .9773 |     .2805      .2019      .9773 
eco_genera~1 |     1.895      .7781      .8958 |     1.895      .7354      .9299 
     lr_no_1 |     .1147      .1016      2.418 |     .1147      .1016      2.418 
polinteres~1 |     3.179      .4515     -.3309 |     3.179      .4567     -.4357 
talk_polit~1 |     4.716      2.031      -.768 |     4.716      2.013      -.709 
pol_comple~1 |     2.434      .9562       .118 |     2.434       1.02      .1746 
internal_e~1 |     2.153       .988      .4234 |     2.153      .9738      .3348 
(dropped 6 singleton observations)
(MWFE estimator converged in 3 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      4,156
Absorbing 2 HDFE groups                           F(   1,    872) =       3.49
Statistics robust to heteroskedasticity           Prob > F        =     0.0622
                                                  R-squared       =     0.3476
                                                  Adj R-squared   =     0.1735
Number of clusters (id)      =      2,081         Within R-sq.    =     0.0010
Number of clusters (comune_id) =        873       Root MSE        =     0.2775

                            (Std. err. adjusted for 873 clusters in id comune_id)
---------------------------------------------------------------------------------
                |               Robust
        turnout | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
m5s_wn_wave_157 |   .0407358   .0218182     1.87   0.062    -.0020865    .0835582
          _cons |   .8681509    .014915    58.21   0.000     .8388773    .8974245
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   comune_id |       873         873           0    *|
        post |         2           0           2     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est10 stored)

.         ** plot all coefficients: 
.         coefplot ///
>         est1 est2 est3 est4 est5, legend(off) bylabel("{bf:Referendum: 'Yes'}")  ///
>         || ///
>         est6 est7 est8 est9 est10,    /// 
>         drop(_cons) ///
>         ciopts(lwidth(0.4 ..)) mlwidth(medthick) msize(medsmall) mfcolor(white) ///
>         byopts(legend(off)) ///
>         bylabel("{bf:Referendum: turnout}") ///
>         xline(0)  ///
>         grid(none) 

.         ** save as pdf: 
.         graph export "$figures/figa11_otheroutcomes.pdf", replace
file /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/results/figures/figa11_otheroutcomes.pdf saved as PDF
    format

.         
.         // summary statistics (a13)
.         ** clear from previous results: 
.         est clear  

.         ** summarize:
.         estpost tabstat referendum_no_wunsure m5s_wn_wave_157 ///
>         eco_general unemployed_3 female_3 age_3 edu_3 religious_3 m5s_1 pd_1 eco_general_1 lr_no_1 polinterest_1 talk_politics_1 e
> co_general_1 lr_no_1 pol_complex_1 internal_efficacy_1 ///
>         if (wave == 3 | wave == 4) & referendum_no_wunsure !=., ///
>         c(stat) stat(mean sd min max n)

Summary statistics: mean sd min max count
     for variables: referendum_no_wunsure m5s_wn_wave_157 eco_general unemployed_3 female_3 age_3 edu_3 religious_3 m5s_1 pd_1 eco_g
> eneral_1 lr_no_1 polinterest_1 talk_politics_1 eco_general_1 lr_no_1 pol_complex_1 internal_efficacy_1

             |   e(mean)      e(sd)     e(min)     e(max)   e(count) 
-------------+-------------------------------------------------------
re~o_wunsure |  .4482065   .4973519          0          1       5966 
m5s_wn_w~157 |  .8603854   1.002037          0   5.006316       5282 
 eco_general |   2.63091   .8881634          1          5       5901 
unemployed_3 |   .426584   .4946222          0          1       5966 
    female_3 |  .4750251   .4994177          0          1       5966 
       age_3 |  48.13289   17.09978         18         88       5922 
       edu_3 |  5.009119      1.407          1          7       5922 
 religious_3 |  .8298693   .3757791          0          1       5966 
       m5s_1 |  .2214083   .4152438          0          1       4232 
        pd_1 |  .2743384    .446233          0          1       4232 
eco_genera~1 |  1.838332   .8507988          1          5       5301 
     lr_no_1 |  .1731634    .378424          0          1       5336 
polinteres~1 |  3.018993   .7693922          1          4       5265 
talk_polit~1 |  4.513689   1.542254          1          6       5223 
eco_genera~1 |  1.838332   .8507988          1          5       5301 
     lr_no_1 |  .1731634    .378424          0          1       5336 
pol_comple~1 |  2.344385   .9935715          1          4       5218 
internal_e~1 |  2.078665   .9849907          1          4       5212 

.         ** save: 
.         esttab using "$tables/taba13_summary_individual.tex", replace ///
>         cells("mean(fmt(%13.2fc)) sd(fmt(%13.2fc)) min max count") nonumber ///
>         nomtitle nonote noobs label collabels("Mean" "SD" "Min" "Max" "N")
(output written to /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/results/tables/taba13_summary_individual.tex)

. 
end of do-file
      name:  plog_33
       log:  /Users/au660280/Dropbox/m5S/JoP_PlaceBasedCampaigning_replication/code/do/10_analysis_individual.log
  log type:  text
 closed on:  19 Sep 2022, 11:40:59
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